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class="pagination-link " aria-label="Page 5" aria-current="page">5 </a> </li> <li> <a href="/search/?searchtype=author&query=Liu%2C+F&start=250" class="pagination-link " aria-label="Page 6" aria-current="page">6 </a> </li> <li> <a href="/search/?searchtype=author&query=Liu%2C+F&start=300" class="pagination-link " aria-label="Page 7" aria-current="page">7 </a> </li> </ul> </nav> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.12002">arXiv:2502.12002</a> <span> [<a href="https://arxiv.org/pdf/2502.12002">pdf</a>, <a href="https://arxiv.org/format/2502.12002">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> NaturalL2S: End-to-End High-quality Multispeaker Lip-to-Speech Synthesis with Differential Digital Signal Processing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liang%2C+Y">Yifan Liang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fangkun Liu</a>, <a href="/search/eess?searchtype=author&query=Li%2C+A">Andong Li</a>, <a href="/search/eess?searchtype=author&query=Li%2C+X">Xiaodong Li</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+C">Chengshi Zheng</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.12002v1-abstract-short" style="display: inline;"> Recent advancements in visual speech recognition (VSR) have promoted progress in lip-to-speech synthesis, where pre-trained VSR models enhance the intelligibility of synthesized speech by providing valuable semantic information. The success achieved by cascade frameworks, which combine pseudo-VSR with pseudo-text-to-speech (TTS) or implicitly utilize the transcribed text, highlights the benefits o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.12002v1-abstract-full').style.display = 'inline'; document.getElementById('2502.12002v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.12002v1-abstract-full" style="display: none;"> Recent advancements in visual speech recognition (VSR) have promoted progress in lip-to-speech synthesis, where pre-trained VSR models enhance the intelligibility of synthesized speech by providing valuable semantic information. The success achieved by cascade frameworks, which combine pseudo-VSR with pseudo-text-to-speech (TTS) or implicitly utilize the transcribed text, highlights the benefits of leveraging VSR models. However, these methods typically rely on mel-spectrograms as an intermediate representation, which may introduce a key bottleneck: the domain gap between synthetic mel-spectrograms, generated from inherently error-prone lip-to-speech mappings, and real mel-spectrograms used to train vocoders. This mismatch inevitably degrades synthesis quality. To bridge this gap, we propose Natural Lip-to-Speech (NaturalL2S), an end-to-end framework integrating acoustic inductive biases with differentiable speech generation components. Specifically, we introduce a fundamental frequency (F0) predictor to capture prosodic variations in synthesized speech. The predicted F0 then drives a Differentiable Digital Signal Processing (DDSP) synthesizer to generate a coarse signal which serves as prior information for subsequent speech synthesis. Additionally, instead of relying on a reference speaker embedding as an auxiliary input, our approach achieves satisfactory performance on speaker similarity without explicitly modelling speaker characteristics. Both objective and subjective evaluation results demonstrate that NaturalL2S can effectively enhance the quality of the synthesized speech when compared to state-of-the-art methods. Our demonstration page is accessible at https://yifan-liang.github.io/NaturalL2S/. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.12002v1-abstract-full').style.display = 'none'; document.getElementById('2502.12002v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.04735">arXiv:2502.04735</a> <span> [<a href="https://arxiv.org/pdf/2502.04735">pdf</a>, <a href="https://arxiv.org/format/2502.04735">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Affine Frequency Division Multiplexing: Extending OFDM for Scenario-Flexibility and Resilience </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yin%2C+H">Haoran Yin</a>, <a href="/search/eess?searchtype=author&query=Tang%2C+Y">Yanqun Tang</a>, <a href="/search/eess?searchtype=author&query=Bemani%2C+A">Ali Bemani</a>, <a href="/search/eess?searchtype=author&query=Kountouris%2C+M">Marios Kountouris</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+Y">Yu Zhou</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xingyao Zhang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Yuqing Liu</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+G">Gaojie Chen</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+K">Kai Yang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/eess?searchtype=author&query=Masouros%2C+C">Christos Masouros</a>, <a href="/search/eess?searchtype=author&query=Li%2C+S">Shuangyang Li</a>, <a href="/search/eess?searchtype=author&query=Caire%2C+G">Giuseppe Caire</a>, <a href="/search/eess?searchtype=author&query=Xiao%2C+P">Pei Xiao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.04735v1-abstract-short" style="display: inline;"> Next-generation wireless networks are conceived to provide reliable and high-data-rate communication services for diverse scenarios, such as vehicle-to-vehicle, unmanned aerial vehicles, and satellite networks. The severe Doppler spreads in the underlying time-varying channels induce destructive inter-carrier interference (ICI) in the extensively adopted orthogonal frequency division multiplexing… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04735v1-abstract-full').style.display = 'inline'; document.getElementById('2502.04735v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.04735v1-abstract-full" style="display: none;"> Next-generation wireless networks are conceived to provide reliable and high-data-rate communication services for diverse scenarios, such as vehicle-to-vehicle, unmanned aerial vehicles, and satellite networks. The severe Doppler spreads in the underlying time-varying channels induce destructive inter-carrier interference (ICI) in the extensively adopted orthogonal frequency division multiplexing (OFDM) waveform, leading to severe performance degradation. This calls for a new air interface design that can accommodate the severe delay-Doppler spreads in highly dynamic channels while possessing sufficient flexibility to cater to various applications. This article provides a comprehensive overview of a promising chirp-based waveform named affine frequency division multiplexing (AFDM). It is featured with two tunable parameters and achieves optimal diversity order in doubly dispersive channels (DDC). We study the fundamental principle of AFDM, illustrating its intrinsic suitability for DDC. Based on that, several potential applications of AFDM are explored. Furthermore, the major challenges and the corresponding solutions of AFDM are presented, followed by several future research directions. Finally, we draw some instructive conclusions about AFDM, hoping to provide useful inspiration for its development. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04735v1-abstract-full').style.display = 'none'; document.getElementById('2502.04735v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Magazine paper submitted to IEEE</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.19142">arXiv:2501.19142</a> <span> [<a href="https://arxiv.org/pdf/2501.19142">pdf</a>, <a href="https://arxiv.org/ps/2501.19142">ps</a>, <a href="https://arxiv.org/format/2501.19142">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> An Integrated Sensing and Communications System Based on Affine Frequency Division Multiplexing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ni%2C+Y">Yuanhan Ni</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+P">Peng Yuan</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+Q">Qin Huang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Z">Zulin Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.19142v1-abstract-short" style="display: inline;"> This paper proposes an integrated sensing and communications (ISAC) system based on affine frequency division multiplexing (AFDM) waveform. To this end, a metric set is designed according to not only the maximum tolerable delay/Doppler, but also the weighted spectral efficiency as well as the outage/error probability of sensing and communications. This enables the analytical investigation of the p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.19142v1-abstract-full').style.display = 'inline'; document.getElementById('2501.19142v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.19142v1-abstract-full" style="display: none;"> This paper proposes an integrated sensing and communications (ISAC) system based on affine frequency division multiplexing (AFDM) waveform. To this end, a metric set is designed according to not only the maximum tolerable delay/Doppler, but also the weighted spectral efficiency as well as the outage/error probability of sensing and communications. This enables the analytical investigation of the performance trade-offs of AFDM-ISAC system using the derived analytical relation among metrics and AFDM waveform parameters. Moreover, by revealing that delay and the integral/fractional parts of normalized Doppler can be decoupled in the affine Fourier transform-Doppler domain, an efficient estimation method is proposed for our AFDM-ISAC system, whose unambiguous Doppler can break through the limitation of subcarrier spacing. Theoretical analyses and numerical results verify that our proposed AFDM-ISAC system may significantly enlarge unambiguous delay/Doppler while possessing good spectral efficiency and peak-to-sidelobe level ratio in high-mobility scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.19142v1-abstract-full').style.display = 'none'; document.getElementById('2501.19142v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.11583">arXiv:2501.11583</a> <span> [<a href="https://arxiv.org/pdf/2501.11583">pdf</a>, <a href="https://arxiv.org/format/2501.11583">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Joint Optimization of Geometric and Probabilistic Constellation Shaping for OFDM-ISAC Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Geiger%2C+B">Benedikt Geiger</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/eess?searchtype=author&query=Lu%2C+S">Shihang Lu</a>, <a href="/search/eess?searchtype=author&query=Rode%2C+A">Andrej Rode</a>, <a href="/search/eess?searchtype=author&query=Schmalen%2C+L">Laurent Schmalen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.11583v1-abstract-short" style="display: inline;"> 6G communications systems are expected to integrate radar-like sensing capabilities enabling novel use cases. However, integrated sensing and communications (ISAC) introduces a trade-off between communications and sensing performance because the optimal constellations for each task differ. In this paper, we compare geometric, probabilistic and joint constellation shaping for orthogonal frequency d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.11583v1-abstract-full').style.display = 'inline'; document.getElementById('2501.11583v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.11583v1-abstract-full" style="display: none;"> 6G communications systems are expected to integrate radar-like sensing capabilities enabling novel use cases. However, integrated sensing and communications (ISAC) introduces a trade-off between communications and sensing performance because the optimal constellations for each task differ. In this paper, we compare geometric, probabilistic and joint constellation shaping for orthogonal frequency division multiplexing (OFDM)-ISAC systems using an autoencoder (AE) framework. We first derive the constellation-dependent detection probability and propose a novel loss function to include the sensing performance in the AE framework. Our simulation results demonstrate that constellation shaping enables a dynamic trade-off between communications and sensing. Depending on whether sensing or communications performance is prioritized, geometric or probabilistic constellation shaping is preferred. Joint constellation shaping combines the advantages of geometric and probabilistic shaping, significantly outperforming legacy modulation formats. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.11583v1-abstract-full').style.display = 'none'; document.getElementById('2501.11583v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at 5th IEEE International Symposium on Joint Communications and Sensing (JC&S), Oulu, Finland</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.10670">arXiv:2501.10670</a> <span> [<a href="https://arxiv.org/pdf/2501.10670">pdf</a>, <a href="https://arxiv.org/format/2501.10670">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Computing Capacity-Cost Functions for Continuous Channels in Wasserstein Space </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Li%2C+X">Xinyang Li</a>, <a href="/search/eess?searchtype=author&query=Andrei%2C+V+C">Vlad C. Andrei</a>, <a href="/search/eess?searchtype=author&query=M%C3%B6nich%2C+U+J">Ullrich J. M枚nich</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/eess?searchtype=author&query=Boche%2C+H">Holger Boche</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.10670v2-abstract-short" style="display: inline;"> This paper investigates the problem of computing capacity-cost (C-C) functions for continuous channels. Motivated by the Kullback-Leibler divergence (KLD) proximal reformulation of the classical Blahut-Arimoto (BA) algorithm, the Wasserstein distance is introduced to the proximal term for the continuous case, resulting in an iterative algorithm related to the Wasserstein gradient descent. Practica… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.10670v2-abstract-full').style.display = 'inline'; document.getElementById('2501.10670v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.10670v2-abstract-full" style="display: none;"> This paper investigates the problem of computing capacity-cost (C-C) functions for continuous channels. Motivated by the Kullback-Leibler divergence (KLD) proximal reformulation of the classical Blahut-Arimoto (BA) algorithm, the Wasserstein distance is introduced to the proximal term for the continuous case, resulting in an iterative algorithm related to the Wasserstein gradient descent. Practical implementation involves moving particles along the negative gradient direction of the objective function's first variation in the Wasserstein space and approximating integrals by the importance sampling (IS) technique. Such formulation is also applied to the rate-distortion (R-D) function for continuous source spaces and thus provides a unified computation framework for both problems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.10670v2-abstract-full').style.display = 'none'; document.getElementById('2501.10670v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to IEEE International Conference on Communications 2025</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.09815">arXiv:2501.09815</a> <span> [<a href="https://arxiv.org/pdf/2501.09815">pdf</a>, <a href="https://arxiv.org/format/2501.09815">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Lossy Compression with Pretrained Diffusion Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Vonderfecht%2C+J">Jeremy Vonderfecht</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Feng Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.09815v1-abstract-short" style="display: inline;"> We apply the DiffC algorithm (Theis et al. 2022) to Stable Diffusion 1.5, 2.1, XL, and Flux-dev, and demonstrate that these pretrained models are remarkably capable lossy image compressors. A principled algorithm for lossy compression using pretrained diffusion models has been understood since at least Ho et al. 2020, but challenges in reverse-channel coding have prevented such algorithms from eve… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.09815v1-abstract-full').style.display = 'inline'; document.getElementById('2501.09815v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.09815v1-abstract-full" style="display: none;"> We apply the DiffC algorithm (Theis et al. 2022) to Stable Diffusion 1.5, 2.1, XL, and Flux-dev, and demonstrate that these pretrained models are remarkably capable lossy image compressors. A principled algorithm for lossy compression using pretrained diffusion models has been understood since at least Ho et al. 2020, but challenges in reverse-channel coding have prevented such algorithms from ever being fully implemented. We introduce simple workarounds that lead to the first complete implementation of DiffC, which is capable of compressing and decompressing images using Stable Diffusion in under 10 seconds. Despite requiring no additional training, our method is competitive with other state-of-the-art generative compression methods at low ultra-low bitrates. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.09815v1-abstract-full').style.display = 'none'; document.getElementById('2501.09815v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.09305">arXiv:2501.09305</a> <span> [<a href="https://arxiv.org/pdf/2501.09305">pdf</a>, <a href="https://arxiv.org/format/2501.09305">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> </div> </div> <p class="title is-5 mathjax"> Domain-conditioned and Temporal-guided Diffusion Modeling for Accelerated Dynamic MRI Reconstruction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+L">Liping Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+I+Y">Iris Yuwen Zhou</a>, <a href="/search/eess?searchtype=author&query=Montesi%2C+S+B">Sydney B. Montesi</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+L">Li Feng</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fang Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.09305v1-abstract-short" style="display: inline;"> Purpose: To propose a domain-conditioned and temporal-guided diffusion modeling method, termed dynamic Diffusion Modeling (dDiMo), for accelerated dynamic MRI reconstruction, enabling diffusion process to characterize spatiotemporal information for time-resolved multi-coil Cartesian and non-Cartesian data. Methods: The dDiMo framework integrates temporal information from time-resolved dimensions,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.09305v1-abstract-full').style.display = 'inline'; document.getElementById('2501.09305v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.09305v1-abstract-full" style="display: none;"> Purpose: To propose a domain-conditioned and temporal-guided diffusion modeling method, termed dynamic Diffusion Modeling (dDiMo), for accelerated dynamic MRI reconstruction, enabling diffusion process to characterize spatiotemporal information for time-resolved multi-coil Cartesian and non-Cartesian data. Methods: The dDiMo framework integrates temporal information from time-resolved dimensions, allowing for the concurrent capture of intra-frame spatial features and inter-frame temporal dynamics in diffusion modeling. It employs additional spatiotemporal ($x$-$t$) and self-consistent frequency-temporal ($k$-$t$) priors to guide the diffusion process. This approach ensures precise temporal alignment and enhances the recovery of fine image details. To facilitate a smooth diffusion process, the nonlinear conjugate gradient algorithm is utilized during the reverse diffusion steps. The proposed model was tested on two types of MRI data: Cartesian-acquired multi-coil cardiac MRI and Golden-Angle-Radial-acquired multi-coil free-breathing lung MRI, across various undersampling rates. Results: dDiMo achieved high-quality reconstructions at various acceleration factors, demonstrating improved temporal alignment and structural recovery compared to other competitive reconstruction methods, both qualitatively and quantitatively. This proposed diffusion framework exhibited robust performance in handling both Cartesian and non-Cartesian acquisitions, effectively reconstructing dynamic datasets in cardiac and lung MRI under different imaging conditions. Conclusion: This study introduces a novel diffusion modeling method for dynamic MRI reconstruction. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.09305v1-abstract-full').style.display = 'none'; document.getElementById('2501.09305v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">21 pages, 15 figures, 2 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.05442">arXiv:2501.05442</a> <span> [<a href="https://arxiv.org/pdf/2501.05442">pdf</a>, <a href="https://arxiv.org/format/2501.05442">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Progressive Growing of Video Tokenizers for Highly Compressed Latent Spaces </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Mahapatra%2C+A">Aniruddha Mahapatra</a>, <a href="/search/eess?searchtype=author&query=Mai%2C+L">Long Mai</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yitian Zhang</a>, <a href="/search/eess?searchtype=author&query=Bourgin%2C+D">David Bourgin</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Feng Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.05442v1-abstract-short" style="display: inline;"> Video tokenizers are essential for latent video diffusion models, converting raw video data into spatiotemporally compressed latent spaces for efficient training. However, extending state-of-the-art video tokenizers to achieve a temporal compression ratio beyond 4x without increasing channel capacity poses significant challenges. In this work, we propose an alternative approach to enhance temporal… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.05442v1-abstract-full').style.display = 'inline'; document.getElementById('2501.05442v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.05442v1-abstract-full" style="display: none;"> Video tokenizers are essential for latent video diffusion models, converting raw video data into spatiotemporally compressed latent spaces for efficient training. However, extending state-of-the-art video tokenizers to achieve a temporal compression ratio beyond 4x without increasing channel capacity poses significant challenges. In this work, we propose an alternative approach to enhance temporal compression. We find that the reconstruction quality of temporally subsampled videos from a low-compression encoder surpasses that of high-compression encoders applied to original videos. This indicates that high-compression models can leverage representations from lower-compression models. Building on this insight, we develop a bootstrapped high-temporal-compression model that progressively trains high-compression blocks atop well-trained lower-compression models. Our method includes a cross-level feature-mixing module to retain information from the pretrained low-compression model and guide higher-compression blocks to capture the remaining details from the full video sequence. Evaluation of video benchmarks shows that our method significantly improves reconstruction quality while increasing temporal compression compared to direct extensions of existing video tokenizers. Furthermore, the resulting compact latent space effectively trains a video diffusion model for high-quality video generation with a reduced token budget. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.05442v1-abstract-full').style.display = 'none'; document.getElementById('2501.05442v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Project website: https://progressive-video-tokenizer.github.io/Pro-MAG/</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.01721">arXiv:2501.01721</a> <span> [<a href="https://arxiv.org/pdf/2501.01721">pdf</a>, <a href="https://arxiv.org/format/2501.01721">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Uncovering the Iceberg in the Sea: Fundamentals of Pulse Shaping and Modulation Design for Random ISAC Signals </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/eess?searchtype=author&query=Xiong%2C+Y">Yifeng Xiong</a>, <a href="/search/eess?searchtype=author&query=Lu%2C+S">Shihang Lu</a>, <a href="/search/eess?searchtype=author&query=Li%2C+S">Shuangyang Li</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+W">Weijie Yuan</a>, <a href="/search/eess?searchtype=author&query=Masouros%2C+C">Christos Masouros</a>, <a href="/search/eess?searchtype=author&query=Jin%2C+S">Shi Jin</a>, <a href="/search/eess?searchtype=author&query=Caire%2C+G">Giuseppe Caire</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.01721v1-abstract-short" style="display: inline;"> Integrated Sensing and Communications (ISAC) is expected to play a pivotal role in future 6G networks. To maximize time-frequency resource utilization, 6G ISAC systems must exploit data payload signals, that are inherently random, for both communication and sensing tasks. This paper provides a comprehensive analysis of the sensing performance of such communication-centric ISAC signals, with a focu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.01721v1-abstract-full').style.display = 'inline'; document.getElementById('2501.01721v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.01721v1-abstract-full" style="display: none;"> Integrated Sensing and Communications (ISAC) is expected to play a pivotal role in future 6G networks. To maximize time-frequency resource utilization, 6G ISAC systems must exploit data payload signals, that are inherently random, for both communication and sensing tasks. This paper provides a comprehensive analysis of the sensing performance of such communication-centric ISAC signals, with a focus on modulation and pulse shaping design to reshape the statistical properties of their auto-correlation functions (ACFs), thereby improving the target ranging performance. We derive a closed-form expression for the expectation of the squared ACF of random ISAC signals, considering arbitrary modulation bases and constellation mappings within the Nyquist pulse shaping framework. The structure is metaphorically described as an ``iceberg hidden in the sea", where the ``iceberg'' represents the squared mean of the ACF of random ISAC signals, that is determined by the pulse shaping filter, and the ``sea level'' characterizes the corresponding variance, caused by the randomness of the data payload. Our analysis shows that, for QAM/PSK constellations with Nyquist pulse shaping, Orthogonal Frequency Division Multiplexing (OFDM) achieves the lowest ranging sidelobe level across all lags. Building on these insights, we propose a novel Nyquist pulse shaping design to enhance the sensing performance of random ISAC signals. Numerical results validate our theoretical findings, showing that the proposed pulse shaping significantly reduces ranging sidelobes compared to conventional root-raised cosine (RRC) pulse shaping, thereby improving the ranging performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.01721v1-abstract-full').style.display = 'none'; document.getElementById('2501.01721v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages, 7 figures, submitted to IEEE for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.00486">arXiv:2412.00486</a> <span> [<a href="https://arxiv.org/pdf/2412.00486">pdf</a>, <a href="https://arxiv.org/format/2412.00486">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Geophysics">physics.geo-ph</span> </div> </div> <p class="title is-5 mathjax"> Automatic Differentiation-based Full Waveform Inversion with Flexible Workflows </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+F">Feng Liu</a>, <a href="/search/eess?searchtype=author&query=Li%2C+H">Haipeng Li</a>, <a href="/search/eess?searchtype=author&query=Zou%2C+G">Guangyuan Zou</a>, <a href="/search/eess?searchtype=author&query=Li%2C+J">Junlun Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.00486v1-abstract-short" style="display: inline;"> Full waveform inversion (FWI) is able to construct high-resolution subsurface models by iteratively minimizing discrepancies between observed and simulated seismic data. However, its implementation can be rather involved for complex wave equations, objective functions, or regularization. Recently, automatic differentiation (AD) has proven to be effective in simplifying solutions of various inverse… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.00486v1-abstract-full').style.display = 'inline'; document.getElementById('2412.00486v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.00486v1-abstract-full" style="display: none;"> Full waveform inversion (FWI) is able to construct high-resolution subsurface models by iteratively minimizing discrepancies between observed and simulated seismic data. However, its implementation can be rather involved for complex wave equations, objective functions, or regularization. Recently, automatic differentiation (AD) has proven to be effective in simplifying solutions of various inverse problems, including FWI. In this study, we present an open-source AD-based FWI framework (ADFWI), which is designed to simplify the design, development, and evaluation of novel approaches in FWI with flexibility. The AD-based framework not only includes forword modeling and associated gradient computations for wave equations in various types of media from isotropic acoustic to vertically or horizontally transverse isotropic elastic, but also incorporates a suite of objective functions, regularization techniques, and optimization algorithms. By leveraging state-of-the-art AD, objective functions such as soft dynamic time warping and Wasserstein distance, which are difficult to apply in traditional FWI are also easily integrated into ADFWI. In addition, ADFWI is integrated with deep learning for implicit model reparameterization via neural networks, which not only introduces learned regularization but also allows rapid estimation of uncertainty through dropout. To manage high memory demands in large-scale inversion associated with AD, the proposed framework adopts strategies such as mini-batch and checkpointing. Through comprehensive evaluations, we demonstrate the novelty, practicality and robustness of ADFWI, which can be used to address challenges in FWI and as a workbench for prompt experiments and the development of new inversion strategies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.00486v1-abstract-full').style.display = 'none'; document.getElementById('2412.00486v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Manuscript including 14 pages supplement. Code link: https://github.com/liufeng2317/ADFWI</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.11687">arXiv:2411.11687</a> <span> [<a href="https://arxiv.org/pdf/2411.11687">pdf</a>, <a href="https://arxiv.org/format/2411.11687">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Coevolution of Opinion Dynamics and Recommendation System: Modeling Analysis and Reinforcement Learning Based Manipulation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+Y">Yuhong Chen</a>, <a href="/search/eess?searchtype=author&query=Dai%2C+X">Xiaobing Dai</a>, <a href="/search/eess?searchtype=author&query=Buss%2C+M">Martin Buss</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fangzhou Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.11687v1-abstract-short" style="display: inline;"> In this work, we develop an analytical framework that integrates opinion dynamics with a recommendation system. By incorporating elements such as collaborative filtering, we provide a precise characterization of how recommendation systems shape interpersonal interactions and influence opinion formation. Moreover, the property of the coevolution of both opinion dynamics and recommendation systems i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11687v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11687v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11687v1-abstract-full" style="display: none;"> In this work, we develop an analytical framework that integrates opinion dynamics with a recommendation system. By incorporating elements such as collaborative filtering, we provide a precise characterization of how recommendation systems shape interpersonal interactions and influence opinion formation. Moreover, the property of the coevolution of both opinion dynamics and recommendation systems is also shown. Specifically, the convergence of this coevolutionary system is theoretically proved, and the mechanisms behind filter bubble formation are elucidated. Our analysis of the maximum number of opinion clusters shows how recommendation system parameters affect opinion grouping and polarization. Additionally, we incorporate the influence of propagators into our model and propose a reinforcement learning-based solution. The analysis and the propagation solution are demonstrated in simulation using the Yelp data set. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11687v1-abstract-full').style.display = 'none'; document.getElementById('2411.11687v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.06687">arXiv:2411.06687</a> <span> [<a href="https://arxiv.org/pdf/2411.06687">pdf</a>, <a href="https://arxiv.org/format/2411.06687">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> An Overview on IRS-Enabled Sensing and Communications for 6G: Architectures, Fundamental Limits, and Joint Beamforming Designs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Song%2C+X">Xianxin Song</a>, <a href="/search/eess?searchtype=author&query=Fang%2C+Y">Yuan Fang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+F">Feng Wang</a>, <a href="/search/eess?searchtype=author&query=Ren%2C+Z">Zixiang Ren</a>, <a href="/search/eess?searchtype=author&query=Yu%2C+X">Xianghao Yu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Ye Zhang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+J">Jie Xu</a>, <a href="/search/eess?searchtype=author&query=Ng%2C+D+W+K">Derrick Wing Kwan Ng</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+R">Rui Zhang</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.06687v1-abstract-short" style="display: inline;"> This paper presents an overview on intelligent reflecting surface (IRS)-enabled sensing and communication for the forthcoming sixth-generation (6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication (S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's (B… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.06687v1-abstract-full').style.display = 'inline'; document.getElementById('2411.06687v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.06687v1-abstract-full" style="display: none;"> This paper presents an overview on intelligent reflecting surface (IRS)-enabled sensing and communication for the forthcoming sixth-generation (6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication (S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's (BS's) non-line-of-sight (NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication (ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.06687v1-abstract-full').style.display = 'none'; document.getElementById('2411.06687v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">22 pages,7 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.21144">arXiv:2410.21144</a> <span> [<a href="https://arxiv.org/pdf/2410.21144">pdf</a>, <a href="https://arxiv.org/format/2410.21144">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Enhancing Learned Image Compression via Cross Window-based Attention </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Mudgal%2C+P">Priyanka Mudgal</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Feng Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.21144v4-abstract-short" style="display: inline;"> In recent years, learned image compression methods have demonstrated superior rate-distortion performance compared to traditional image compression methods. Recent methods utilize convolutional neural networks (CNN), variational autoencoders (VAE), invertible neural networks (INN), and transformers. Despite their significant contributions, a main drawback of these models is their poor performance… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.21144v4-abstract-full').style.display = 'inline'; document.getElementById('2410.21144v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.21144v4-abstract-full" style="display: none;"> In recent years, learned image compression methods have demonstrated superior rate-distortion performance compared to traditional image compression methods. Recent methods utilize convolutional neural networks (CNN), variational autoencoders (VAE), invertible neural networks (INN), and transformers. Despite their significant contributions, a main drawback of these models is their poor performance in capturing local redundancy. Therefore, to leverage global features along with local redundancy, we propose a CNN-based solution integrated with a feature encoding module. The feature encoding module encodes important features before feeding them to the CNN and then utilizes cross-scale window-based attention, which further captures local redundancy. Cross-scale window-based attention is inspired by the attention mechanism in transformers and effectively enlarges the receptive field. Both the feature encoding module and the cross-scale window-based attention module in our architecture are flexible and can be incorporated into any other network architecture. We evaluate our method on the Kodak and CLIC datasets and demonstrate that our approach is effective and on par with state-of-the-art methods. Our code is publicly available at https://github.com/prmudgal/CWAM_IC_ISVC. . <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.21144v4-abstract-full').style.display = 'none'; document.getElementById('2410.21144v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Paper accepted and presented in ISVC'24. Copyrights stay with ISVC Our code is available at: https://github.com/prmudgal/CWAM_IC_ISVC</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.17494">arXiv:2410.17494</a> <span> [<a href="https://arxiv.org/pdf/2410.17494">pdf</a>, <a href="https://arxiv.org/format/2410.17494">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ding%2C+J">Jun-En Ding</a>, <a href="/search/eess?searchtype=author&query=Hsu%2C+C">Chien-Chin Hsu</a>, <a href="/search/eess?searchtype=author&query=Chu%2C+C">Chi-Hsiang Chu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+S">Shuqiang Wang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Feng Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.17494v3-abstract-short" style="display: inline;"> The classification of medical images is a pivotal aspect of disease diagnosis, often enhanced by deep learning techniques. However, traditional approaches typically focus on unimodal medical image data, neglecting the integration of diverse non-image patient data. This paper proposes a novel Cross-Graph Modal Contrastive Learning (CGMCL) framework for multimodal structured data from different data… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.17494v3-abstract-full').style.display = 'inline'; document.getElementById('2410.17494v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.17494v3-abstract-full" style="display: none;"> The classification of medical images is a pivotal aspect of disease diagnosis, often enhanced by deep learning techniques. However, traditional approaches typically focus on unimodal medical image data, neglecting the integration of diverse non-image patient data. This paper proposes a novel Cross-Graph Modal Contrastive Learning (CGMCL) framework for multimodal structured data from different data domains to improve medical image classification. The model effectively integrates both image and non-image data by constructing cross-modality graphs and leveraging contrastive learning to align multimodal features in a shared latent space. An inter-modality feature scaling module further optimizes the representation learning process by reducing the gap between heterogeneous modalities. The proposed approach is evaluated on two datasets: a Parkinson's disease (PD) dataset and a public melanoma dataset. Results demonstrate that CGMCL outperforms conventional unimodal methods in accuracy, interpretability, and early disease prediction. Additionally, the method shows superior performance in multi-class melanoma classification. The CGMCL framework provides valuable insights into medical image classification while offering improved disease interpretability and predictive capabilities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.17494v3-abstract-full').style.display = 'none'; document.getElementById('2410.17494v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.15646">arXiv:2410.15646</a> <span> [<a href="https://arxiv.org/pdf/2410.15646">pdf</a>, <a href="https://arxiv.org/format/2410.15646">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Low-Complexity Minimum BER Precoder Design for ISAC Systems: A Delay-Doppler Perspective </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wu%2C+J">Jun Wu</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+W">Weijie Yuan</a>, <a href="/search/eess?searchtype=author&query=Wei%2C+Z">Zhiqiang Wei</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+K">Kecheng Zhang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/eess?searchtype=author&query=Ng%2C+D+W+K">Derrick Wing Kwan Ng</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.15646v1-abstract-short" style="display: inline;"> Orthogonal time frequency space (OTFS) modulation is anticipated to be a promising candidate for supporting integrated sensing and communications (ISAC) systems, which is considered as a pivotal technique for realizing next generation wireless networks. In this paper, we develop a minimum bit error rate (BER) precoder design for an OTFS-based ISAC system. In particular, the BER minimization proble… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.15646v1-abstract-full').style.display = 'inline'; document.getElementById('2410.15646v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.15646v1-abstract-full" style="display: none;"> Orthogonal time frequency space (OTFS) modulation is anticipated to be a promising candidate for supporting integrated sensing and communications (ISAC) systems, which is considered as a pivotal technique for realizing next generation wireless networks. In this paper, we develop a minimum bit error rate (BER) precoder design for an OTFS-based ISAC system. In particular, the BER minimization problem takes into account the maximum available transmission power budget and the required sensing performance. Different from prior studies that considered ISAC in the time-frequency (TF) domain, we devise the precoder from the perspective of the delay-Doppler (DD) domain by exploiting the equivalent DD domain channel due to the fact that the DD domain channel generally tends to be sparse and quasi-static, which can facilitate a low-overhead ISAC system design. To address the non-convex optimization design problem, we resort to optimizing the lower bound of the derived average BER by adopting Jensen's inequality. Subsequently, the formulated problem is decoupled into two independent sub-problems via singular value decomposition (SVD) methodology. We then theoretically analyze the feasibility conditions of the proposed problem and present a low-complexity iterative solution via leveraging the Lagrangian duality approach. Simulation results verify the effectiveness of our proposed precoder compared to the benchmark schemes and reveal the interplay between sensing and communication for dual-functional precoder design, indicating a trade-off where transmission efficiency is sacrificed for increasing transmission reliability and sensing accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.15646v1-abstract-full').style.display = 'none'; document.getElementById('2410.15646v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.13219">arXiv:2410.13219</a> <span> [<a href="https://arxiv.org/pdf/2410.13219">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Fundamental Limits of Pulse Based UWB ISAC Systems: A Parameter Estimation Perspective </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+T">Tingting Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zenan Zhang</a>, <a href="/search/eess?searchtype=author&query=Cao%2C+B">Bin Cao</a>, <a href="/search/eess?searchtype=author&query=Shen%2C+Y">Yuan Shen</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Q">Qinyu Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.13219v1-abstract-short" style="display: inline;"> Impulse radio ultra-wideband (IR-UWB) signals stand out for their high temporal resolution, low cost, and large bandwidth, making them a highly promising option for integrated sensing and communication (ISAC) systems. In this paper, we design an ISAC system for a bi-static passive sensing scenario that accommodates multiple targets. Specifically, we introduce two typical modulation schemes, PPM an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.13219v1-abstract-full').style.display = 'inline'; document.getElementById('2410.13219v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.13219v1-abstract-full" style="display: none;"> Impulse radio ultra-wideband (IR-UWB) signals stand out for their high temporal resolution, low cost, and large bandwidth, making them a highly promising option for integrated sensing and communication (ISAC) systems. In this paper, we design an ISAC system for a bi-static passive sensing scenario that accommodates multiple targets. Specifically, we introduce two typical modulation schemes, PPM and BPSK, for data transmission. The essential coupling between sensing and communication is examined through the Fisher information matrix (FIM). Accordingly, we introduce a pilot-based decoupling approach that relies on known time-delays, as well as a differential decoupling strategy that uses a known starting symbol position. Finally, we assess the sensing and communication performance under various modulation and demodulation schemes under the constraints of current UWB standards. This assessment utilizes the Cramer-Rao Lower Bound (CRLB) for sensing and the Shannon capacity limit for communication, offering theoretical insights into choosing suitable data signal processing methods in real-world applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.13219v1-abstract-full').style.display = 'none'; document.getElementById('2410.13219v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.04990">arXiv:2410.04990</a> <span> [<a href="https://arxiv.org/pdf/2410.04990">pdf</a>, <a href="https://arxiv.org/format/2410.04990">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Stage-Wise and Prior-Aware Neural Speech Phase Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fei Liu</a>, <a href="/search/eess?searchtype=author&query=Ai%2C+Y">Yang Ai</a>, <a href="/search/eess?searchtype=author&query=Du%2C+H">Hui-Peng Du</a>, <a href="/search/eess?searchtype=author&query=Lu%2C+Y">Ye-Xin Lu</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+R">Rui-Chen Zheng</a>, <a href="/search/eess?searchtype=author&query=Ling%2C+Z">Zhen-Hua Ling</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.04990v1-abstract-short" style="display: inline;"> This paper proposes a novel Stage-wise and Prior-aware Neural Speech Phase Prediction (SP-NSPP) model, which predicts the phase spectrum from input amplitude spectrum by two-stage neural networks. In the initial prior-construction stage, we preliminarily predict a rough prior phase spectrum from the amplitude spectrum. The subsequent refinement stage transforms the amplitude spectrum into a refine… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.04990v1-abstract-full').style.display = 'inline'; document.getElementById('2410.04990v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.04990v1-abstract-full" style="display: none;"> This paper proposes a novel Stage-wise and Prior-aware Neural Speech Phase Prediction (SP-NSPP) model, which predicts the phase spectrum from input amplitude spectrum by two-stage neural networks. In the initial prior-construction stage, we preliminarily predict a rough prior phase spectrum from the amplitude spectrum. The subsequent refinement stage transforms the amplitude spectrum into a refined high-quality phase spectrum conditioned on the prior phase. Networks in both stages use ConvNeXt v2 blocks as the backbone and adopt adversarial training by innovatively introducing a phase spectrum discriminator (PSD). To further improve the continuity of the refined phase, we also incorporate a time-frequency integrated difference (TFID) loss in the refinement stage. Experimental results confirm that, compared to neural network-based no-prior phase prediction methods, the proposed SP-NSPP achieves higher phase prediction accuracy, thanks to introducing the coarse phase priors and diverse training criteria. Compared to iterative phase estimation algorithms, our proposed SP-NSPP does not require multiple rounds of staged iterations, resulting in higher generation efficiency. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.04990v1-abstract-full').style.display = 'none'; document.getElementById('2410.04990v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by SLT2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.03178">arXiv:2410.03178</a> <span> [<a href="https://arxiv.org/pdf/2410.03178">pdf</a>, <a href="https://arxiv.org/format/2410.03178">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Optimal Control in Both Steady State and Transient Process with Unknown Disturbances </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Li%2C+M">Ming Li</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Z">Zhaojian Wang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Feng Liu</a>, <a href="/search/eess?searchtype=author&query=Cao%2C+M">Ming Cao</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+B">Bo Yang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.03178v1-abstract-short" style="display: inline;"> The scheme of online optimization as a feedback controller is widely used to steer the states of a physical system to the optimal solution of a predefined optimization problem. Such methods focus on regulating the physical states to the optimal solution in the steady state, without considering the performance during the transient process. In this paper, we simultaneously consider the performance i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03178v1-abstract-full').style.display = 'inline'; document.getElementById('2410.03178v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.03178v1-abstract-full" style="display: none;"> The scheme of online optimization as a feedback controller is widely used to steer the states of a physical system to the optimal solution of a predefined optimization problem. Such methods focus on regulating the physical states to the optimal solution in the steady state, without considering the performance during the transient process. In this paper, we simultaneously consider the performance in both the steady state and the transient process of a linear time-invariant system with unknown disturbances. The performance of the transient process is illustrated by the concept of overtaking optimality. An overtaking optimal controller with known disturbances is derived to achieve the transient overtaking optimality while guaranteeing steady-state performance. Then, we propose a disturbance independent near-optimal controller, which can achieve optimal steady-state performance and approach the overtaking optimal performance in the transient process. The system performance gap between the overtaking optimal controller and the proposed controller proves to be inversely proportional to the control gains. A case study on a power system with four buses is used to validate the effectiveness of the two controllers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03178v1-abstract-full').style.display = 'none'; document.getElementById('2410.03178v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.20453">arXiv:2409.20453</a> <span> [<a href="https://arxiv.org/pdf/2409.20453">pdf</a>, <a href="https://arxiv.org/format/2409.20453">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> E-Healthcare Systems: Integrated Sensing, Computing, and Semantic Communication with Physical Layer Security </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yang%2C+Y">Yinchao Yang</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+Z">Zhaohui Yang</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+W">Weijie Yuan</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/eess?searchtype=author&query=Cao%2C+X">Xiaowen Cao</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+C">Chongwen Huang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zhaoyang Zhang</a>, <a href="/search/eess?searchtype=author&query=Shikh-Bahaei%2C+M">Mohammad Shikh-Bahaei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.20453v1-abstract-short" style="display: inline;"> This paper introduces an integrated sensing, computing, and semantic communication (ISCSC) framework tailored for smart healthcare systems. The framework is evaluated in the context of smart healthcare, optimising the transmit beamforming matrix and semantic extraction ratio for improved data rates, sensing accuracy, and general data protection regulation (GDPR) compliance, while considering IoRT… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.20453v1-abstract-full').style.display = 'inline'; document.getElementById('2409.20453v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.20453v1-abstract-full" style="display: none;"> This paper introduces an integrated sensing, computing, and semantic communication (ISCSC) framework tailored for smart healthcare systems. The framework is evaluated in the context of smart healthcare, optimising the transmit beamforming matrix and semantic extraction ratio for improved data rates, sensing accuracy, and general data protection regulation (GDPR) compliance, while considering IoRT device computing capabilities. Semantic metrics such as semantic transmission rate and semantic secrecy rate are derived to evaluate data rate performance and GDPR risk, respectively, while the Cram茅r-Rao Bound (CRB) assesses sensing performance. Simulation results demonstrate the framework's effectiveness in ensuring reliable sensing, high data rates, and secure communication. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.20453v1-abstract-full').style.display = 'none'; document.getElementById('2409.20453v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been accepted by GLOBECOM 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.18742">arXiv:2409.18742</a> <span> [<a href="https://arxiv.org/pdf/2409.18742">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> </div> </div> <p class="title is-5 mathjax"> A History-Guided Regional Partitioning Evolutionary Optimization for Solving the Flexible Job Shop Problem with Limited Multi-load Automated Guided Vehicles </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+F">Feige Liu</a>, <a href="/search/eess?searchtype=author&query=Lu%2C+C">Chao Lu</a>, <a href="/search/eess?searchtype=author&query=Li%2C+X">Xin Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.18742v1-abstract-short" style="display: inline;"> In a flexible job shop environment, using Automated Guided Vehicles (AGVs) to transport jobs and process materials is an important way to promote the intelligence of the workshop. Compared with single-load AGVs, multi-load AGVs can improve AGV utilization, reduce path conflicts, etc. Therefore, this study proposes a history-guided regional partitioning algorithm (HRPEO) for the flexible job shop s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.18742v1-abstract-full').style.display = 'inline'; document.getElementById('2409.18742v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.18742v1-abstract-full" style="display: none;"> In a flexible job shop environment, using Automated Guided Vehicles (AGVs) to transport jobs and process materials is an important way to promote the intelligence of the workshop. Compared with single-load AGVs, multi-load AGVs can improve AGV utilization, reduce path conflicts, etc. Therefore, this study proposes a history-guided regional partitioning algorithm (HRPEO) for the flexible job shop scheduling problem with limited multi-load AGVs (FJSPMA). First, the encoding and decoding rules are designed according to the characteristics of multi-load AGVs, and then the initialization rule based on the branch and bound method is used to generate the initial population. Second, to prevent the algorithm from falling into a local optimum, the algorithm adopts a regional partitioning strategy. This strategy divides the solution space into multiple regions and measures the potential of the regions. After that, cluster the regions into multiple clusters in each iteration, and selects individuals for evolutionary search based on the set of clusters. Third, a local search strategy is designed to improve the exploitation ability of the algorithm, which uses a greedy approach to optimize machines selection and transportation sequence according to the characteristics of FJSPMA. Finally, a large number of experiments are carried out on the benchmarks to test the performance of the algorithm. Compared with multiple advanced algorithms, the results show that the HRPEO has a better advantage in solving FJSPMA. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.18742v1-abstract-full').style.display = 'none'; document.getElementById('2409.18742v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.18524">arXiv:2409.18524</a> <span> [<a href="https://arxiv.org/pdf/2409.18524">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Adaptive Knowledge-based Multi-Objective Evolutionary Algorithm for Hybrid Flow Shop Scheduling Problems with Multiple Parallel Batch Processing Stages </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+F">Feige Liu</a>, <a href="/search/eess?searchtype=author&query=Li%2C+X">Xin Li</a>, <a href="/search/eess?searchtype=author&query=Lu%2C+C">Chao Lu</a>, <a href="/search/eess?searchtype=author&query=Gong%2C+W">Wenying Gong</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.18524v1-abstract-short" style="display: inline;"> Parallel batch processing machines have extensive applications in the semiconductor manufacturing process. However, the problem models in previous studies regard parallel batch processing as a fixed processing stage in the machining process. This study generalizes the problem model, in which users can arbitrarily set certain stages as parallel batch processing stages according to their needs. A Hy… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.18524v1-abstract-full').style.display = 'inline'; document.getElementById('2409.18524v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.18524v1-abstract-full" style="display: none;"> Parallel batch processing machines have extensive applications in the semiconductor manufacturing process. However, the problem models in previous studies regard parallel batch processing as a fixed processing stage in the machining process. This study generalizes the problem model, in which users can arbitrarily set certain stages as parallel batch processing stages according to their needs. A Hybrid Flow Shop Scheduling Problem with Parallel Batch Processing Machines (PBHFSP) is solved in this paper. Furthermore, an Adaptive Knowledge-based Multi-Objective Evolutionary Algorithm (AMOEA/D) is designed to simultaneously optimize both makespan and Total Energy Consumption (TEC). Firstly, a hybrid initialization strategy with heuristic rules based on knowledge of PBHFSP is proposed to generate promising solutions. Secondly, the disjunctive graph model has been established based on the knowledge to find the critical-path of PBHFS. Then, a critical-path based neighborhood search is proposed to enhance the exploitation ability of AMOEA/D. Moreover, the search time is adaptively adjusted based on learning experience from Q-learning and Decay Law. Afterward, to enhance the exploration capability of the algorithm, AMOEA/D designs an improved population updating strategy with a weight vector updating strategy. These strategies rematch individuals with weight vectors, thereby maintaining the diversity of the population. Finally, the proposed algorithm is compared with state-of-the-art algorithms. The experimental results show that the AMOEA/D is superior to the comparison algorithms in solving the PBHFSP. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.18524v1-abstract-full').style.display = 'none'; document.getElementById('2409.18524v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.08143">arXiv:2409.08143</a> <span> [<a href="https://arxiv.org/pdf/2409.08143">pdf</a>, <a href="https://arxiv.org/format/2409.08143">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Effective Segmentation of Post-Treatment Gliomas Using Simple Approaches: Artificial Sequence Generation and Ensemble Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Kim%2C+H">Heejong Kim</a>, <a href="/search/eess?searchtype=author&query=Milecki%2C+L">Leo Milecki</a>, <a href="/search/eess?searchtype=author&query=Moghadam%2C+M+C">Mina C Moghadam</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fengbei Liu</a>, <a href="/search/eess?searchtype=author&query=Nguyen%2C+M">Minh Nguyen</a>, <a href="/search/eess?searchtype=author&query=Qiu%2C+E">Eric Qiu</a>, <a href="/search/eess?searchtype=author&query=Thanki%2C+A">Abhishek Thanki</a>, <a href="/search/eess?searchtype=author&query=Sabuncu%2C+M+R">Mert R Sabuncu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.08143v1-abstract-short" style="display: inline;"> Segmentation is a crucial task in the medical imaging field and is often an important primary step or even a prerequisite to the analysis of medical volumes. Yet treatments such as surgery complicate the accurate delineation of regions of interest. The BraTS Post-Treatment 2024 Challenge published the first public dataset for post-surgery glioma segmentation and addresses the aforementioned issue… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.08143v1-abstract-full').style.display = 'inline'; document.getElementById('2409.08143v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.08143v1-abstract-full" style="display: none;"> Segmentation is a crucial task in the medical imaging field and is often an important primary step or even a prerequisite to the analysis of medical volumes. Yet treatments such as surgery complicate the accurate delineation of regions of interest. The BraTS Post-Treatment 2024 Challenge published the first public dataset for post-surgery glioma segmentation and addresses the aforementioned issue by fostering the development of automated segmentation tools for glioma in MRI data. In this effort, we propose two straightforward approaches to enhance the segmentation performances of deep learning-based methodologies. First, we incorporate an additional input based on a simple linear combination of the available MRI sequences input, which highlights enhancing tumors. Second, we employ various ensembling methods to weigh the contribution of a battery of models. Our results demonstrate that these approaches significantly improve segmentation performance compared to baseline models, underscoring the effectiveness of these simple approaches in improving medical image segmentation tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.08143v1-abstract-full').style.display = 'none'; document.getElementById('2409.08143v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Invited for an Oral Presentation at the MICCAI BraTS Challenge 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.06307">arXiv:2409.06307</a> <span> [<a href="https://arxiv.org/pdf/2409.06307">pdf</a>, <a href="https://arxiv.org/format/2409.06307">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> An End-to-End Approach for Chord-Conditioned Song Generation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Gao%2C+S">Shuochen Gao</a>, <a href="/search/eess?searchtype=author&query=Lei%2C+S">Shun Lei</a>, <a href="/search/eess?searchtype=author&query=Zhuo%2C+F">Fan Zhuo</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+H">Hangyu Liu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Feng Liu</a>, <a href="/search/eess?searchtype=author&query=Tang%2C+B">Boshi Tang</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+Q">Qiaochu Huang</a>, <a href="/search/eess?searchtype=author&query=Kang%2C+S">Shiyin Kang</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+Z">Zhiyong Wu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.06307v1-abstract-short" style="display: inline;"> The Song Generation task aims to synthesize music composed of vocals and accompaniment from given lyrics. While the existing method, Jukebox, has explored this task, its constrained control over the generations often leads to deficiency in music performance. To mitigate the issue, we introduce an important concept from music composition, namely chords, to song generation networks. Chords form the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.06307v1-abstract-full').style.display = 'inline'; document.getElementById('2409.06307v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.06307v1-abstract-full" style="display: none;"> The Song Generation task aims to synthesize music composed of vocals and accompaniment from given lyrics. While the existing method, Jukebox, has explored this task, its constrained control over the generations often leads to deficiency in music performance. To mitigate the issue, we introduce an important concept from music composition, namely chords, to song generation networks. Chords form the foundation of accompaniment and provide vocal melody with associated harmony. Given the inaccuracy of automatic chord extractors, we devise a robust cross-attention mechanism augmented with dynamic weight sequence to integrate extracted chord information into song generations and reduce frame-level flaws, and propose a novel model termed Chord-Conditioned Song Generator (CSG) based on it. Experimental evidence demonstrates our proposed method outperforms other approaches in terms of musical performance and control precision of generated songs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.06307v1-abstract-full').style.display = 'none'; document.getElementById('2409.06307v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.06029">arXiv:2409.06029</a> <span> [<a href="https://arxiv.org/pdf/2409.06029">pdf</a>, <a href="https://arxiv.org/format/2409.06029">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> SongCreator: Lyrics-based Universal Song Generation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Lei%2C+S">Shun Lei</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+Y">Yixuan Zhou</a>, <a href="/search/eess?searchtype=author&query=Tang%2C+B">Boshi Tang</a>, <a href="/search/eess?searchtype=author&query=Lam%2C+M+W+Y">Max W. Y. Lam</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Feng Liu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+H">Hangyu Liu</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+J">Jingcheng Wu</a>, <a href="/search/eess?searchtype=author&query=Kang%2C+S">Shiyin Kang</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+Z">Zhiyong Wu</a>, <a href="/search/eess?searchtype=author&query=Meng%2C+H">Helen Meng</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.06029v2-abstract-short" style="display: inline;"> Music is an integral part of human culture, embodying human intelligence and creativity, of which songs compose an essential part. While various aspects of song generation have been explored by previous works, such as singing voice, vocal composition and instrumental arrangement, etc., generating songs with both vocals and accompaniment given lyrics remains a significant challenge, hindering the a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.06029v2-abstract-full').style.display = 'inline'; document.getElementById('2409.06029v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.06029v2-abstract-full" style="display: none;"> Music is an integral part of human culture, embodying human intelligence and creativity, of which songs compose an essential part. While various aspects of song generation have been explored by previous works, such as singing voice, vocal composition and instrumental arrangement, etc., generating songs with both vocals and accompaniment given lyrics remains a significant challenge, hindering the application of music generation models in the real world. In this light, we propose SongCreator, a song-generation system designed to tackle this challenge. The model features two novel designs: a meticulously designed dual-sequence language model (DSLM) to capture the information of vocals and accompaniment for song generation, and a series of attention mask strategies for DSLM, which allows our model to understand, generate and edit songs, making it suitable for various songrelated generation tasks by utilizing specific attention masks. Extensive experiments demonstrate the effectiveness of SongCreator by achieving state-of-the-art or competitive performances on all eight tasks. Notably, it surpasses previous works by a large margin in lyrics-to-song and lyrics-to-vocals. Additionally, it is able to independently control the acoustic conditions of the vocals and accompaniment in the generated song through different audio prompts, exhibiting its potential applicability. Our samples are available at https://thuhcsi.github.io/SongCreator/. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.06029v2-abstract-full').style.display = 'none'; document.getElementById('2409.06029v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by NeurIPS 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.08602">arXiv:2408.08602</a> <span> [<a href="https://arxiv.org/pdf/2408.08602">pdf</a>, <a href="https://arxiv.org/format/2408.08602">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Discrete-time SIS Social Contagion Processes on Hypergraphs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liang%2C+L">Lidan Liang</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shaoxuan Cui</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fangzhou Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.08602v1-abstract-short" style="display: inline;"> Recent research on social contagion processes has revealed the limitations of traditional networks, which capture only pairwise relationships, to characterize complex multiparty relationships and group influences properly. Social contagion processes on higher-order networks (simplicial complexes and general hypergraphs) have therefore emerged as a novel frontier. In this work, we investigate discr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.08602v1-abstract-full').style.display = 'inline'; document.getElementById('2408.08602v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.08602v1-abstract-full" style="display: none;"> Recent research on social contagion processes has revealed the limitations of traditional networks, which capture only pairwise relationships, to characterize complex multiparty relationships and group influences properly. Social contagion processes on higher-order networks (simplicial complexes and general hypergraphs) have therefore emerged as a novel frontier. In this work, we investigate discrete-time Susceptible-Infected-Susceptible (SIS) social contagion processes occurring on weighted and directed hypergraphs and their extensions to bivirus cases and general higher-order SIS processes with the aid of tensor algebra. Our focus lies in comprehensively characterizing the healthy state and endemic equilibria within this framework. The emergence of bistability or multistability behavior phenomena, where multiple equilibria coexist and are simultaneously locally asymptotically stable, is demonstrated in view of the presence of the higher-order interaction. The novel sufficient conditions of the appearance for system behaviors, which are determined by both (higher-order) network topology and transition rates, are provided to assess the likelihood of the SIS social contagion processes causing an outbreak. More importantly, given the equilibrium is locally stable, an explicit domain of attraction associated with the system parameters is constructed. Moreover, a learning method to estimate the transition rates is presented. In the end, the attained theoretical results are supplemented via numerical examples. Specifically, we evaluate the effectiveness of the networked SIS social contagion process by comparing it with the $2^n$-state Markov chain model. These numerical examples are given to highlight the performance of parameter learning algorithms and the system behaviors of the discrete-time SIS social contagion process. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.08602v1-abstract-full').style.display = 'none'; document.getElementById('2408.08602v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.06049">arXiv:2408.06049</a> <span> [<a href="https://arxiv.org/pdf/2408.06049">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Hardware Architecture Design of Model-Based Image Reconstruction Towards Palm-size Photoacoustic Tomography </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zheng%2C+Y">Yuwei Zheng</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+Z">Zijian Gao</a>, <a href="/search/eess?searchtype=author&query=Shen%2C+Y">Yuting Shen</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+J">Jiadong Zhang</a>, <a href="/search/eess?searchtype=author&query=Jiang%2C+D">Daohuai Jiang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fengyu Liu</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+F">Feng Gao</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+F">Fei Gao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.06049v1-abstract-short" style="display: inline;"> Photoacoustic (PA) imaging technology combines the advantages of optical imaging and ultrasound imaging, showing great potential in biomedical applications. Many preclinical studies and clinical applications urgently require fast, high-quality, low-cost and portable imaging system. Translating advanced image reconstruction algorithms into hardware implementations is highly desired. However, existi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.06049v1-abstract-full').style.display = 'inline'; document.getElementById('2408.06049v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.06049v1-abstract-full" style="display: none;"> Photoacoustic (PA) imaging technology combines the advantages of optical imaging and ultrasound imaging, showing great potential in biomedical applications. Many preclinical studies and clinical applications urgently require fast, high-quality, low-cost and portable imaging system. Translating advanced image reconstruction algorithms into hardware implementations is highly desired. However, existing iterative PA image reconstructions, although exhibit higher accuracy than delay-and-sum algorithm, suffer from high computational cost. In this paper, we introduce a model-based hardware acceleration architecture based on superposed Wave (s-Wave) for palm-size PA tomography (palm-PAT), aiming at enhancing both the speed and performance of image reconstruction at a much lower system cost. To achieve this, we propose an innovative data reuse method that significantly reduces hardware storage resource consumption. We conducted experiments by FPGA implementation of the algorithm, using both phantoms and in vivo human finger data to verify the feasibility of the proposed method. The results demonstrate that our proposed architecture can substantially reduce system cost while maintaining high imaging performance. The hardware-accelerated implementation of the model-based algorithm achieves a speedup of up to approximately 270 times compared to the CPU, while the corresponding energy efficiency ratio is improved by more than 2700 times. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.06049v1-abstract-full').style.display = 'none'; document.getElementById('2408.06049v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 13 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.06045">arXiv:2408.06045</a> <span> [<a href="https://arxiv.org/pdf/2408.06045">pdf</a>, <a href="https://arxiv.org/format/2408.06045">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Dynamical Systems">math.DS</span> </div> </div> <p class="title is-5 mathjax"> DC-DC Converters Optimization in Case of Large Variation in the Load </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Domyshev%2C+A">Alexander Domyshev</a>, <a href="/search/eess?searchtype=author&query=Chistyakova%2C+E">Elena Chistyakova</a>, <a href="/search/eess?searchtype=author&query=Dreglea%2C+A">Aliona Dreglea</a>, <a href="/search/eess?searchtype=author&query=Sidorov%2C+D">Denis Sidorov</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fang Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.06045v1-abstract-short" style="display: inline;"> The method for controlling a DC-DC converter is proposed to ensures the high quality control at large fluctuations in load currents by using differential gain control coefficients and second derivative control. Various implementations of balancing the currents of a multiphase DC-DC converter are discussed, with a focus on achieving accurate current regulation without introducing additional delay i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.06045v1-abstract-full').style.display = 'inline'; document.getElementById('2408.06045v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.06045v1-abstract-full" style="display: none;"> The method for controlling a DC-DC converter is proposed to ensures the high quality control at large fluctuations in load currents by using differential gain control coefficients and second derivative control. Various implementations of balancing the currents of a multiphase DC-DC converter are discussed, with a focus on achieving accurate current regulation without introducing additional delay in the control system. Stochastic particle swarm optimization method is used to find optimal values of the PID controller parameters. An automatic constraint-handling in optimization are also discussed as relevant techniques in the field. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.06045v1-abstract-full').style.display = 'none'; document.getElementById('2408.06045v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 34H05 34A09 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.15530">arXiv:2407.15530</a> <span> [<a href="https://arxiv.org/pdf/2407.15530">pdf</a>, <a href="https://arxiv.org/ps/2407.15530">ps</a>, <a href="https://arxiv.org/format/2407.15530">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Pulse Shaping for Random ISAC Signals: The Ambiguity Function Between Symbols Matters </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liao%2C+Z">Zihan Liao</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/eess?searchtype=author&query=Li%2C+S">Shuangyang Li</a>, <a href="/search/eess?searchtype=author&query=Xiong%2C+Y">Yifeng Xiong</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+W">Weijie Yuan</a>, <a href="/search/eess?searchtype=author&query=Masouros%2C+C">Christos Masouros</a>, <a href="/search/eess?searchtype=author&query=Lops%2C+M">Marco Lops</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.15530v1-abstract-short" style="display: inline;"> Integrated sensing and communications (ISAC) has emerged as a pivotal enabling technology for next-generation wireless networks. Despite the distinct signal design requirements of sensing and communication (S&C) systems, shifting the symbol-wise pulse shaping (SWiPS) framework from communication-only systems to ISAC poses significant challenges in signal design and processing This paper addresses… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.15530v1-abstract-full').style.display = 'inline'; document.getElementById('2407.15530v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.15530v1-abstract-full" style="display: none;"> Integrated sensing and communications (ISAC) has emerged as a pivotal enabling technology for next-generation wireless networks. Despite the distinct signal design requirements of sensing and communication (S&C) systems, shifting the symbol-wise pulse shaping (SWiPS) framework from communication-only systems to ISAC poses significant challenges in signal design and processing This paper addresses these challenges by examining the ambiguity function (AF) of the SWiPS ISAC signal and introducing a novel pulse shaping design for single-carrier ISAC transmission. We formulate optimization problems to minimize the average integrated sidelobe level (ISL) of the AF, as well as the weighted ISL (WISL) while satisfying inter-symbol interference (ISI), out-of-band emission (OOBE), and power constraints. Our contributions include establishing the relationship between the AFs of both the random data symbols and signaling pulses, analyzing the statistical characteristics of the AF, and developing algorithmic frameworks for pulse shaping optimization using successive convex approximation (SCA) and alternating direction method of multipliers (ADMM) approaches. Numerical results are provided to validate our theoretical analysis, which demonstrate significant performance improvements in the proposed SWiPS design compared to the root-raised cosine (RRC) pulse shaping for conventional communication systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.15530v1-abstract-full').style.display = 'none'; document.getElementById('2407.15530v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.13401">arXiv:2407.13401</a> <span> [<a href="https://arxiv.org/pdf/2407.13401">pdf</a>, <a href="https://arxiv.org/format/2407.13401">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Cooperative Integrated Sensing and Communication Networks: Analysis and Distributed Design </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+B">Bowen Wang</a>, <a href="/search/eess?searchtype=author&query=Li%2C+H">Hongyu Li</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/eess?searchtype=author&query=Cheng%2C+Z">Ziyang Cheng</a>, <a href="/search/eess?searchtype=author&query=Shen%2C+S">Shanpu Shen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.13401v1-abstract-short" style="display: inline;"> This paper proposes a cooperative integrated sensing and communication network (Co-ISACNet) adopting hybrid beamforming (HBF) architecture, which improves both radar sensing and communication performance. The main contributions of this work are four-fold. First, we introduce a novel cooperative sensing method for the considered Co-ISACNet, followed by a comprehensive analysis of this method. This… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.13401v1-abstract-full').style.display = 'inline'; document.getElementById('2407.13401v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.13401v1-abstract-full" style="display: none;"> This paper proposes a cooperative integrated sensing and communication network (Co-ISACNet) adopting hybrid beamforming (HBF) architecture, which improves both radar sensing and communication performance. The main contributions of this work are four-fold. First, we introduce a novel cooperative sensing method for the considered Co-ISACNet, followed by a comprehensive analysis of this method. This analysis mathematically verifies the benefits of Co-ISACNet and provides insightful design guidelines. Second, to show the benefits of Co-ISACNet, we propose to jointly design the HBF to maximize the network communication capacity while satisfying the constraint of beampattern similarity for radar sensing, which results in a highly dimensional and non-convex problem. Third, to facilitate the joint design, we propose a novel distributed optimization framework based on proximal gradient and alternating direction method of multipliers, namely PANDA. Fourth, we further adopt the proposed PANDA framework to solve the joint HBF design problem for the Co-ISACNet. By using the proposed PANDA framework, all access points (APs) optimize the HBF in parallel, where each AP only requires local channel state information and limited message exchange among the APs. Such framework reduces significantly the computational complexity and thus has pronounced benefits in practical scenarios. Simulation results verify the effectiveness of the proposed algorithm compared with the conventional centralized algorithm and show the remarkable performance improvement of radar sensing and communication by deploying Co-ISACNet. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.13401v1-abstract-full').style.display = 'none'; document.getElementById('2407.13401v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.12184">arXiv:2407.12184</a> <span> [<a href="https://arxiv.org/pdf/2407.12184">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> The object detection method aids in image reconstruction evaluation and clinical interpretation of meniscal abnormalities </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Konovalova%2C+N">Natalia Konovalova</a>, <a href="/search/eess?searchtype=author&query=Tolpadi%2C+A">Aniket Tolpadi</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Felix Liu</a>, <a href="/search/eess?searchtype=author&query=Akkaya%2C+Z">Zehra Akkaya</a>, <a href="/search/eess?searchtype=author&query=Gassert%2C+F">Felix Gassert</a>, <a href="/search/eess?searchtype=author&query=Giesler%2C+P">Paula Giesler</a>, <a href="/search/eess?searchtype=author&query=Luitjens%2C+J">Johanna Luitjens</a>, <a href="/search/eess?searchtype=author&query=Han%2C+M">Misung Han</a>, <a href="/search/eess?searchtype=author&query=Bahroos%2C+E">Emma Bahroos</a>, <a href="/search/eess?searchtype=author&query=Majumdar%2C+S">Sharmila Majumdar</a>, <a href="/search/eess?searchtype=author&query=Pedoia%2C+V">Valentina Pedoia</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.12184v1-abstract-short" style="display: inline;"> This study investigates the relationship between deep learning (DL) image reconstruction quality and anomaly detection performance, and evaluates the efficacy of an artificial intelligence (AI) assistant in enhancing radiologists' interpretation of meniscal anomalies on reconstructed images. A retrospective study was conducted using an in-house reconstruction and anomaly detection pipeline to asse… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.12184v1-abstract-full').style.display = 'inline'; document.getElementById('2407.12184v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.12184v1-abstract-full" style="display: none;"> This study investigates the relationship between deep learning (DL) image reconstruction quality and anomaly detection performance, and evaluates the efficacy of an artificial intelligence (AI) assistant in enhancing radiologists' interpretation of meniscal anomalies on reconstructed images. A retrospective study was conducted using an in-house reconstruction and anomaly detection pipeline to assess knee MR images from 896 patients. The original and 14 sets of DL-reconstructed images were evaluated using standard reconstruction and object detection metrics, alongside newly developed box-based reconstruction metrics. Two clinical radiologists reviewed a subset of 50 patients' images, both original and AI-assisted reconstructed, with subsequent assessment of their accuracy and performance characteristics. Results indicated that the structural similarity index (SSIM) showed a weaker correlation with anomaly detection metrics (mAP, r=0.64, p=0.01; F1 score, r=0.38, p=0.18), while box-based SSIM had a stronger association with detection performance (mAP, r=0.81, p<0.01; F1 score, r=0.65, p=0.01). Minor SSIM fluctuations did not affect detection outcomes, but significant changes reduced performance. Radiologists' AI-assisted evaluations demonstrated improved accuracy (86.0% without assistance vs. 88.3% with assistance, p<0.05) and interrater agreement (Cohen's kappa, 0.39 without assistance vs. 0.57 with assistance). An additional review led to the incorporation of 17 more lesions into the dataset. The proposed anomaly detection method shows promise in evaluating reconstruction algorithms for automated tasks and aiding radiologists in interpreting DL-reconstructed MR images. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.12184v1-abstract-full').style.display = 'none'; document.getElementById('2407.12184v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.08591">arXiv:2407.08591</a> <span> [<a href="https://arxiv.org/pdf/2407.08591">pdf</a>, <a href="https://arxiv.org/format/2407.08591">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> 6D Motion Parameters Estimation in Monostatic Integrated Sensing and Communications System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Luo%2C+H">Hongliang Luo</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+F">Feifei Gao</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/eess?searchtype=author&query=Jin%2C+S">Shi Jin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.08591v1-abstract-short" style="display: inline;"> In this paper, we propose a novel scheme to estimate the six dimensional (6D) motion parameters of dynamic target for monostatic integrated sensing and communications (ISAC) system. We first provide a generic ISAC framework for dynamic target sensing based on massive multiple input and multiple output (MIMO) array. Next, we derive the relationship between the sensing channel of ISAC base station (… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.08591v1-abstract-full').style.display = 'inline'; document.getElementById('2407.08591v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.08591v1-abstract-full" style="display: none;"> In this paper, we propose a novel scheme to estimate the six dimensional (6D) motion parameters of dynamic target for monostatic integrated sensing and communications (ISAC) system. We first provide a generic ISAC framework for dynamic target sensing based on massive multiple input and multiple output (MIMO) array. Next, we derive the relationship between the sensing channel of ISAC base station (BS) and the 6D motion parameters of dynamic target. Then, we employ the array signal processing methods to estimate the horizontal angle, pitch angle, distance, and virtual velocity of dynamic target. Since the virtual velocities observed by different antennas are different, we adopt plane fitting to estimate the dynamic target's radial velocity, horizontal angular velocity, and pitch angular velocity from these virtual velocities. Simulation results demonstrate the effectiveness of the proposed 6D motion parameters estimation scheme, which also confirms a new finding that one single BS with massive MIMO array is capable of estimating the horizontal angular velocity and pitch angular velocity of dynamic target. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.08591v1-abstract-full').style.display = 'none'; document.getElementById('2407.08591v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">arXiv admin note: substantial text overlap with arXiv:2312.16441</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.06691">arXiv:2407.06691</a> <span> [<a href="https://arxiv.org/pdf/2407.06691">pdf</a>, <a href="https://arxiv.org/ps/2407.06691">ps</a>, <a href="https://arxiv.org/format/2407.06691">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> OFDM Achieves the Lowest Ranging Sidelobe Under Random ISAC Signaling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Ying Zhang</a>, <a href="/search/eess?searchtype=author&query=Xiong%2C+Y">Yifeng Xiong</a>, <a href="/search/eess?searchtype=author&query=Li%2C+S">Shuangyang Li</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+W">Weijie Yuan</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+F">Feifei Gao</a>, <a href="/search/eess?searchtype=author&query=Jin%2C+S">Shi Jin</a>, <a href="/search/eess?searchtype=author&query=Caire%2C+G">Giuseppe Caire</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.06691v2-abstract-short" style="display: inline;"> This paper aims to answer a fundamental question in the area of Integrated Sensing and Communications (ISAC): What is the optimal communication-centric ISAC waveform for ranging? Towards that end, we first established a generic framework to analyze the sensing performance of communication-centric ISAC waveforms built upon orthonormal signaling bases and random data symbols. Then, we evaluated thei… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.06691v2-abstract-full').style.display = 'inline'; document.getElementById('2407.06691v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.06691v2-abstract-full" style="display: none;"> This paper aims to answer a fundamental question in the area of Integrated Sensing and Communications (ISAC): What is the optimal communication-centric ISAC waveform for ranging? Towards that end, we first established a generic framework to analyze the sensing performance of communication-centric ISAC waveforms built upon orthonormal signaling bases and random data symbols. Then, we evaluated their ranging performance by adopting both the periodic and aperiodic auto-correlation functions (P-ACF and A-ACF), and defined the expectation of the integrated sidelobe level (EISL) as a sensing performance metric. On top of that, we proved that among all communication waveforms with cyclic prefix (CP), the orthogonal frequency division multiplexing (OFDM) modulation is the only globally optimal waveform that achieves the lowest ranging sidelobe for quadrature amplitude modulation (QAM) and phase shift keying (PSK) constellations, in terms of both the EISL and the sidelobe level at each individual lag of the P-ACF. As a step forward, we proved that among all communication waveforms without CP, OFDM is a locally optimal waveform for QAM/PSK in the sense that it achieves a local minimum of the EISL of the A-ACF. Finally, we demonstrated by numerical results that under QAM/PSK constellations, there is no other orthogonal communication-centric waveform that achieves a lower ranging sidelobe level than that of the OFDM, in terms of both P-ACF and A-ACF cases. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.06691v2-abstract-full').style.display = 'none'; document.getElementById('2407.06691v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages, 11 figures, submitted to IEEE for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.04746">arXiv:2407.04746</a> <span> [<a href="https://arxiv.org/pdf/2407.04746">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Moving Target Detection Method Based on Range? Doppler Domain Compensation and Cancellation for UAV-Mounted Radar </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Qu%2C+X">Xiaodong Qu</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+X">Xiaolong Sun</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Feiyang Liu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+H">Hao Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+S">Shichao Zhong</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+X">Xiaopeng Yang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.04746v1-abstract-short" style="display: inline;"> Combining unmanned aerial vehicle (UAV) with through-the-wall radar can realize moving targets detection in complex building scenes. However, clutters generated by obstacles and static objects are always stronger and non-stationary, which results in heavy impacts on moving targets detection. To address this issue, this paper proposes a moving target detection method based on Range-Doppler domain c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.04746v1-abstract-full').style.display = 'inline'; document.getElementById('2407.04746v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.04746v1-abstract-full" style="display: none;"> Combining unmanned aerial vehicle (UAV) with through-the-wall radar can realize moving targets detection in complex building scenes. However, clutters generated by obstacles and static objects are always stronger and non-stationary, which results in heavy impacts on moving targets detection. To address this issue, this paper proposes a moving target detection method based on Range-Doppler domain compensation and cancellation for UAV mounted dual channel radar. In the proposed method, phase compensation is performed on the dual channel in range-Doppler domain and then cancellation is utilized to achieve roughly clutters suppression. Next, a filter is constructed based on the cancellation result and the raw echoes, which is used to suppress stationary clutter furthermore. Finally, mismatch imaging is used to focus moving target for detection. Both simulation and UAV-based experiment results are analyzed to verify the efficacy and practicability of the proposed method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.04746v1-abstract-full').style.display = 'none'; document.getElementById('2407.04746v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.14740">arXiv:2406.14740</a> <span> [<a href="https://arxiv.org/pdf/2406.14740">pdf</a>, <a href="https://arxiv.org/format/2406.14740">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Reachability and Controllability Analysis of the State Covariance for Linear Stochastic Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fengjiao Liu</a>, <a href="/search/eess?searchtype=author&query=Tsiotras%2C+P">Panagiotis Tsiotras</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.14740v1-abstract-short" style="display: inline;"> This paper studies the set of terminal state covariances that are reachable over a finite time horizon from a given initial state covariance for a linear stochastic system with additive noise. For discrete-time systems, a complete characterization of the set of reachable state covariances is given. For continuous-time systems, we present an upper bound on the set of reachable state covariances. Mo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.14740v1-abstract-full').style.display = 'inline'; document.getElementById('2406.14740v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.14740v1-abstract-full" style="display: none;"> This paper studies the set of terminal state covariances that are reachable over a finite time horizon from a given initial state covariance for a linear stochastic system with additive noise. For discrete-time systems, a complete characterization of the set of reachable state covariances is given. For continuous-time systems, we present an upper bound on the set of reachable state covariances. Moreover, for both linear discrete-time and continuous-time systems, necessary and sufficient conditions are provided for the controllability of the state covariance over a finite horizon. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.14740v1-abstract-full').style.display = 'none'; document.getElementById('2406.14740v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages, 1 figure</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.12300">arXiv:2406.12300</a> <span> [<a href="https://arxiv.org/pdf/2406.12300">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> IR2QSM: Quantitative Susceptibility Mapping via Deep Neural Networks with Iterative Reverse Concatenations and Recurrent Modules </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Li%2C+M">Min Li</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+C">Chen Chen</a>, <a href="/search/eess?searchtype=author&query=Xiong%2C+Z">Zhuang Xiong</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Ying Liu</a>, <a href="/search/eess?searchtype=author&query=Rong%2C+P">Pengfei Rong</a>, <a href="/search/eess?searchtype=author&query=Shan%2C+S">Shanshan Shan</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Feng Liu</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+H">Hongfu Sun</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+Y">Yang Gao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.12300v1-abstract-short" style="display: inline;"> Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing technique to extract the distribution of tissue susceptibilities, demonstrating significant potential in studying neurological diseases. However, the ill-conditioned nature of dipole inversion makes QSM reconstruction from the tissue field prone to noise and artifacts. In this work, we propose a novel deep learning-bas… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.12300v1-abstract-full').style.display = 'inline'; document.getElementById('2406.12300v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.12300v1-abstract-full" style="display: none;"> Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing technique to extract the distribution of tissue susceptibilities, demonstrating significant potential in studying neurological diseases. However, the ill-conditioned nature of dipole inversion makes QSM reconstruction from the tissue field prone to noise and artifacts. In this work, we propose a novel deep learning-based IR2QSM method for QSM reconstruction. It is designed by iterating four times of a reverse concatenations and middle recurrent modules enhanced U-net, which could dramatically improve the efficiency of latent feature utilization. Simulated and in vivo experiments were conducted to compare IR2QSM with several traditional algorithms (MEDI and iLSQR) and state-of-the-art deep learning methods (U-net, xQSM, and LPCNN). The results indicated that IR2QSM was able to obtain QSM images with significantly increased accuracy and mitigated artifacts over other methods. Particularly, IR2QSM demonstrated on average the best NRMSE (27.59%) in simulated experiments, which is 15.48%, 7.86%, 17.24%, 9.26%, and 29.13% lower than iLSQR, MEDI, U-net, xQSM, LPCNN, respectively, and led to improved QSM results with fewer artifacts for the in vivo data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.12300v1-abstract-full').style.display = 'none'; document.getElementById('2406.12300v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 9 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.07338">arXiv:2406.07338</a> <span> [<a href="https://arxiv.org/pdf/2406.07338">pdf</a>, <a href="https://arxiv.org/format/2406.07338">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Capacity Credit Evaluation of Generalized Energy Storage Considering Strategic Capacity Withholding and Decision-Dependent Uncertainty </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Qi%2C+N">Ning Qi</a>, <a href="/search/eess?searchtype=author&query=Pinson%2C+P">Pierre Pinson</a>, <a href="/search/eess?searchtype=author&query=Almassalkhi%2C+M+R">Mads R. Almassalkhi</a>, <a href="/search/eess?searchtype=author&query=Zhuang%2C+Y">Yingrui Zhuang</a>, <a href="/search/eess?searchtype=author&query=Su%2C+Y">Yifan Su</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Feng Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.07338v4-abstract-short" style="display: inline;"> This paper proposes a novel capacity credit evaluation framework to accurately quantify the contribution of generalized energy storage (GES) to resource adequacy, considering both strategic capacity withholding and decision-dependent uncertainty (DDU). To this end, we establish a market-oriented risk-averse coordinated dispatch method to capture the cross-market reliable operation of GES. The prop… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.07338v4-abstract-full').style.display = 'inline'; document.getElementById('2406.07338v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.07338v4-abstract-full" style="display: none;"> This paper proposes a novel capacity credit evaluation framework to accurately quantify the contribution of generalized energy storage (GES) to resource adequacy, considering both strategic capacity withholding and decision-dependent uncertainty (DDU). To this end, we establish a market-oriented risk-averse coordinated dispatch method to capture the cross-market reliable operation of GES. The proposed method is sequentially implemented along with the Monte Carlo simulation process, coordinating the pre-dispatched price arbitrage and capacity withholding in the energy market with adequacy-oriented re-dispatch during capacity market calls. In addition to decision-independent uncertainties in operational states and baseline behavior, we explicitly address the inherent DDU of GES (i.e., the uncertainty of available discharge capacity affected by the incentives and accumulated discomfort) during the re-dispatch stage using the proposed distributional robust chance-constrained approach. Furthermore, a capacity credit metric called equivalent storage capacity substitution is introduced to quantify the equivalent deterministic storage capacity of uncertain GES. Simulations on the modified IEEE RTS-79 benchmark system with 20 years real-world data from Elia demonstrate that the proposed method yields accurate capacity credit and improved economic performance. We show that the capacity credit of GES increases with more strategic capacity withholding but decreases with more DDU levels. Key factors, such as capacity withholding and DDU structure impacting GES's capacity credit are analyzed with insights into capacity market decision-making. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.07338v4-abstract-full').style.display = 'none'; document.getElementById('2406.07338v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This is a manuscript submitted to Applied Energy</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.10553">arXiv:2405.10553</a> <span> [<a href="https://arxiv.org/pdf/2405.10553">pdf</a>, <a href="https://arxiv.org/format/2405.10553">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Revealing the Trade-off in ISAC Systems: The KL Divergence Perspective </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Fei%2C+Z">Zesong Fei</a>, <a href="/search/eess?searchtype=author&query=Tang%2C+S">Shuntian Tang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+X">Xinyi Wang</a>, <a href="/search/eess?searchtype=author&query=Xia%2C+F">Fanghao Xia</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+J+A">J. Andrew Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.10553v1-abstract-short" style="display: inline;"> Integrated sensing and communication (ISAC) is regarded as a promising technique for 6G communication network. In this letter, we investigate the Pareto bound of the ISAC system in terms of a unified Kullback-Leibler (KL) divergence performance metric. We firstly present the relationship between KL divergence and explicit ISAC performance metric, i.e., demodulation error and probability of detecti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.10553v1-abstract-full').style.display = 'inline'; document.getElementById('2405.10553v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.10553v1-abstract-full" style="display: none;"> Integrated sensing and communication (ISAC) is regarded as a promising technique for 6G communication network. In this letter, we investigate the Pareto bound of the ISAC system in terms of a unified Kullback-Leibler (KL) divergence performance metric. We firstly present the relationship between KL divergence and explicit ISAC performance metric, i.e., demodulation error and probability of detection. Thereafter, we investigate the impact of constellation and beamforming design on the Pareto bound via deep learning and semi-definite relaxation (SDR) techniques. Simulation results show the trade-off between sensing and communication performance in terms of bit error rate (BER) and probability of detection under different parameter set-ups. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.10553v1-abstract-full').style.display = 'none'; document.getElementById('2405.10553v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 5 figures; submitted to IEEE journals for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.03597">arXiv:2405.03597</a> <span> [<a href="https://arxiv.org/pdf/2405.03597">pdf</a>, <a href="https://arxiv.org/format/2405.03597">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Improving the Ranging Performance of Random ISAC Signals Through Pulse Shaping Design </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liao%2C+Z">Zihan Liao</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/eess?searchtype=author&query=Li%2C+S">Shuangyang Li</a>, <a href="/search/eess?searchtype=author&query=Xiong%2C+Y">Yifeng Xiong</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+W">Weijie Yuan</a>, <a href="/search/eess?searchtype=author&query=Lops%2C+M">Marco Lops</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.03597v2-abstract-short" style="display: inline;"> In this paper, we propose a novel pulse shaping design for single-carrier integrated sensing and communication (ISAC) transmission. Due to the communication information embedded in the ISAC signal, the resulting auto-correlation function (ACF) is determined by both the information-conveying random symbol sequence and the signaling pulse, where the former leads to random fluctuations in the sidelob… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.03597v2-abstract-full').style.display = 'inline'; document.getElementById('2405.03597v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.03597v2-abstract-full" style="display: none;"> In this paper, we propose a novel pulse shaping design for single-carrier integrated sensing and communication (ISAC) transmission. Due to the communication information embedded in the ISAC signal, the resulting auto-correlation function (ACF) is determined by both the information-conveying random symbol sequence and the signaling pulse, where the former leads to random fluctuations in the sidelobes of the ACF, impairing the range estimation performance. To overcome this challenge, we first analyze the statistical characteristics of the random ACF under the symbol-wise pulse shaping (SWPS) regime. As a step further, we formulate an optimization problem to design ISAC pulse shaping filters, which minimizes the average integrated sidelobe level ratio (ISLR) while meeting the Nyquist criterion, subject to power and bandwidth constraints. We then show that the problem can be recast as a convex quadratic program by expressing it in the frequency domain, which can be readily solved through standard tools. Numerical results demonstrate that the proposed pulse shaping design achieves substantial ranging sidelobe reduction compared to the celebrated root-raised cosine (RRC) pulse shaping, given that the communication throughput is unchanged. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.03597v2-abstract-full').style.display = 'none'; document.getElementById('2405.03597v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.09436">arXiv:2404.09436</a> <span> [<a href="https://arxiv.org/pdf/2404.09436">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Image Reconstruction with B0 Inhomogeneity using an Interpretable Deep Unrolled Network on an Open-bore MRI-Linac </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Shan%2C+S">Shanshan Shan</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+Y">Yang Gao</a>, <a href="/search/eess?searchtype=author&query=Waddington%2C+D+E+J">David E. J. Waddington</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+H">Hongli Chen</a>, <a href="/search/eess?searchtype=author&query=Whelan%2C+B">Brendan Whelan</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+P+Z+Y">Paul Z. Y. Liu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yaohui Wang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+C">Chunyi Liu</a>, <a href="/search/eess?searchtype=author&query=Gan%2C+H">Hongping Gan</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+M">Mingyuan Gao</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Feng Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.09436v1-abstract-short" style="display: inline;"> MRI-Linac systems require fast image reconstruction with high geometric fidelity to localize and track tumours for radiotherapy treatments. However, B0 field inhomogeneity distortions and slow MR acquisition potentially limit the quality of the image guidance and tumour treatments. In this study, we develop an interpretable unrolled network, referred to as RebinNet, to reconstruct distortion-free… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.09436v1-abstract-full').style.display = 'inline'; document.getElementById('2404.09436v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.09436v1-abstract-full" style="display: none;"> MRI-Linac systems require fast image reconstruction with high geometric fidelity to localize and track tumours for radiotherapy treatments. However, B0 field inhomogeneity distortions and slow MR acquisition potentially limit the quality of the image guidance and tumour treatments. In this study, we develop an interpretable unrolled network, referred to as RebinNet, to reconstruct distortion-free images from B0 inhomogeneity-corrupted k-space for fast MRI-guided radiotherapy applications. RebinNet includes convolutional neural network (CNN) blocks to perform image regularizations and nonuniform fast Fourier Transform (NUFFT) modules to incorporate B0 inhomogeneity information. The RebinNet was trained on a publicly available MR dataset from eleven healthy volunteers for both fully sampled and subsampled acquisitions. Grid phantom and human brain images acquired from an open-bore 1T MRI-Linac scanner were used to evaluate the performance of the proposed network. The RebinNet was compared with the conventional regularization algorithm and our recently developed UnUNet method in terms of root mean squared error (RMSE), structural similarity (SSIM), residual distortions, and computation time. Imaging results demonstrated that the RebinNet reconstructed images with lowest RMSE (<0.05) and highest SSIM (>0.92) at four-time acceleration for simulated brain images. The RebinNet could better preserve structural details and substantially improve the computational efficiency (ten-fold faster) compared to the conventional regularization methods, and had better generalization ability than the UnUNet method. The proposed RebinNet can achieve rapid image reconstruction and overcome the B0 inhomogeneity distortions simultaneously, which would facilitate accurate and fast image guidance in radiotherapy treatments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.09436v1-abstract-full').style.display = 'none'; document.getElementById('2404.09436v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.08188">arXiv:2404.08188</a> <span> [<a href="https://arxiv.org/pdf/2404.08188">pdf</a>, <a href="https://arxiv.org/format/2404.08188">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Fundamental Limits of Communication-Assisted Sensing in ISAC Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Dong%2C+F">Fuwang Dong</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+S">Shihang Liu</a>, <a href="/search/eess?searchtype=author&query=Xiong%2C+Y">Yifeng Xiong</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+W">Weijie Yuan</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+Y">Yuanhao Cui</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.08188v2-abstract-short" style="display: inline;"> In this paper, we introduce a novel communication-assisted sensing (CAS) framework that explores the potential coordination gains offered by the integrated sensing and communication technique. The CAS system endows users with beyond-line-of-the-sight sensing capabilities, supported by a dual-functional base station that enables simultaneous sensing and communication. To delve into the system's fun… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.08188v2-abstract-full').style.display = 'inline'; document.getElementById('2404.08188v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.08188v2-abstract-full" style="display: none;"> In this paper, we introduce a novel communication-assisted sensing (CAS) framework that explores the potential coordination gains offered by the integrated sensing and communication technique. The CAS system endows users with beyond-line-of-the-sight sensing capabilities, supported by a dual-functional base station that enables simultaneous sensing and communication. To delve into the system's fundamental limits, we characterize the information-theoretic framework of the CAS system in terms of rate-distortion theory. We reveal the achievable overall distortion between the target's state and the reconstructions at the end-user, referred to as the sensing quality of service, within a special case where the distortion metric is separable for sensing and communication processes. As a case study, we employ a typical application to demonstrate distortion minimization under the ISAC signaling strategy, showcasing the potential of CAS in enhancing sensing capabilities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.08188v2-abstract-full').style.display = 'none'; document.getElementById('2404.08188v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been accepted by ISIT. The updated version will be coming soon</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.02663">arXiv:2404.02663</a> <span> [<a href="https://arxiv.org/pdf/2404.02663">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Ground-to-UAV sub-Terahertz channel measurement and modeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Li%2C+D">Da Li</a>, <a href="/search/eess?searchtype=author&query=Li%2C+P">Peian Li</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+J">Jiabiao Zhao</a>, <a href="/search/eess?searchtype=author&query=Liang%2C+J">Jianjian Liang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+J">Jiacheng Liu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+G">Guohao Liu</a>, <a href="/search/eess?searchtype=author&query=Lei%2C+Y">Yuanshuai Lei</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+W">Wenbo Liu</a>, <a href="/search/eess?searchtype=author&query=Deng%2C+J">Jianqin Deng</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fuyong Liu</a>, <a href="/search/eess?searchtype=author&query=Ma%2C+J">Jianjun Ma</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.02663v3-abstract-short" style="display: inline;"> Unmanned Aerial Vehicle (UAV) assisted terahertz (THz) wireless communications have been expected to play a vital role in the next generation of wireless networks. UAVs can serve as either repeaters or data collectors within the communication link, thereby potentially augmenting the efficacy of communication systems. Despite their promise, the channel analysis and modeling specific to THz wireless… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.02663v3-abstract-full').style.display = 'inline'; document.getElementById('2404.02663v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.02663v3-abstract-full" style="display: none;"> Unmanned Aerial Vehicle (UAV) assisted terahertz (THz) wireless communications have been expected to play a vital role in the next generation of wireless networks. UAVs can serve as either repeaters or data collectors within the communication link, thereby potentially augmenting the efficacy of communication systems. Despite their promise, the channel analysis and modeling specific to THz wireless channels leveraging UAVs remain under explored. This work delves into a ground-to-UAV channel at 140 GHz, with a specific focus on the influence of UAV hovering behavior on channel performance. Employing experimental measurements through an unmodulated channel setup and a geometry-based stochastic model (GBSM) that integrates three-dimensional positional coordinates and beamwidth, this work evaluates the impact of UAV dynamic movements and antenna orientation on channel performance. Our findings highlight the minimal impact of UAV orientation adjustments on channel performance and underscore the diminishing necessity for precise alignment between UAVs and ground stations as beamwidth increases. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.02663v3-abstract-full').style.display = 'none'; document.getElementById('2404.02663v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To be published in Optics Express</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.19966">arXiv:2403.19966</a> <span> [<a href="https://arxiv.org/pdf/2403.19966">pdf</a>, <a href="https://arxiv.org/format/2403.19966">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Multi-task Magnetic Resonance Imaging Reconstruction using Meta-learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Bian%2C+W">Wanyu Bian</a>, <a href="/search/eess?searchtype=author&query=Jang%2C+A">Albert Jang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fang Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.19966v2-abstract-short" style="display: inline;"> Using single-task deep learning methods to reconstruct Magnetic Resonance Imaging (MRI) data acquired with different imaging sequences is inherently challenging. The trained deep learning model typically lacks generalizability, and the dissimilarity among image datasets with different types of contrast leads to suboptimal learning performance. This paper proposes a meta-learning approach to effici… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.19966v2-abstract-full').style.display = 'inline'; document.getElementById('2403.19966v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.19966v2-abstract-full" style="display: none;"> Using single-task deep learning methods to reconstruct Magnetic Resonance Imaging (MRI) data acquired with different imaging sequences is inherently challenging. The trained deep learning model typically lacks generalizability, and the dissimilarity among image datasets with different types of contrast leads to suboptimal learning performance. This paper proposes a meta-learning approach to efficiently learn image features from multiple MR image datasets. Our algorithm can perform multi-task learning to simultaneously reconstruct MR images acquired using different imaging sequences with different image contrasts. The experiment results demonstrate the ability of our new meta-learning reconstruction method to successfully reconstruct highly-undersampled k-space data from multiple MRI datasets simultaneously, outperforming other compelling reconstruction methods previously developed for single-task learning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.19966v2-abstract-full').style.display = 'none'; document.getElementById('2403.19966v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.19224">arXiv:2403.19224</a> <span> [<a href="https://arxiv.org/pdf/2403.19224">pdf</a>, <a href="https://arxiv.org/format/2403.19224">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/ICASSP48485.2024.10446974">10.1109/ICASSP48485.2024.10446974 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Emotion Neural Transducer for Fine-Grained Speech Emotion Recognition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Shen%2C+S">Siyuan Shen</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+Y">Yu Gao</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Feng Liu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+H">Hanyang Wang</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+A">Aimin Zhou</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.19224v1-abstract-short" style="display: inline;"> The mainstream paradigm of speech emotion recognition (SER) is identifying the single emotion label of the entire utterance. This line of works neglect the emotion dynamics at fine temporal granularity and mostly fail to leverage linguistic information of speech signal explicitly. In this paper, we propose Emotion Neural Transducer for fine-grained speech emotion recognition with automatic speech… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.19224v1-abstract-full').style.display = 'inline'; document.getElementById('2403.19224v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.19224v1-abstract-full" style="display: none;"> The mainstream paradigm of speech emotion recognition (SER) is identifying the single emotion label of the entire utterance. This line of works neglect the emotion dynamics at fine temporal granularity and mostly fail to leverage linguistic information of speech signal explicitly. In this paper, we propose Emotion Neural Transducer for fine-grained speech emotion recognition with automatic speech recognition (ASR) joint training. We first extend typical neural transducer with emotion joint network to construct emotion lattice for fine-grained SER. Then we propose lattice max pooling on the alignment lattice to facilitate distinguishing emotional and non-emotional frames. To adapt fine-grained SER to transducer inference manner, we further make blank, the special symbol of ASR, serve as underlying emotion indicator as well, yielding Factorized Emotion Neural Transducer. For typical utterance-level SER, our ENT models outperform state-of-the-art methods on IEMOCAP in low word error rate. Experiments on IEMOCAP and the latest speech emotion diarization dataset ZED also demonstrate the superiority of fine-grained emotion modeling. Our code is available at https://github.com/ECNU-Cross-Innovation-Lab/ENT. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.19224v1-abstract-full').style.display = 'none'; document.getElementById('2403.19224v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by 49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.17627">arXiv:2403.17627</a> <span> [<a href="https://arxiv.org/pdf/2403.17627">pdf</a>, <a href="https://arxiv.org/format/2403.17627">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Waveform Design for Joint Communication and SAR Imaging Under Random Signaling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zheng%2C+B">Bowen Zheng</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.17627v1-abstract-short" style="display: inline;"> Conventional synthetic aperture radar (SAR) imaging systems typically employ deterministic signal designs, which lack the capability to convey communication information and are thus not suitable for integrated sensing and communication (ISAC) scenarios. In this letter, we propose a joint communication and SAR imaging (JCASAR) system based on orthogonal frequency-division multiplexing (OFDM) signal… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.17627v1-abstract-full').style.display = 'inline'; document.getElementById('2403.17627v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.17627v1-abstract-full" style="display: none;"> Conventional synthetic aperture radar (SAR) imaging systems typically employ deterministic signal designs, which lack the capability to convey communication information and are thus not suitable for integrated sensing and communication (ISAC) scenarios. In this letter, we propose a joint communication and SAR imaging (JCASAR) system based on orthogonal frequency-division multiplexing (OFDM) signal with cyclic prefix (CP), which is capable of reconstructing the target profile while serving a communication user. In contrast to traditional matched filters, we propose a least squares (LS) estimator for range profiling. Then the SAR image is obtained followed by range cell migration correction (RCMC) and azimuth processing. By minimizing the mean squared error (MSE) of the proposed LS estimator, we investigate the optimal waveform design for SAR imaging, and JCASAR under random signaling, where power allocation strategies are conceived for Gaussian-distributed ISAC signals, in an effort to strike a flexible performance tradeoff between the communication and SAR imaging tasks. Numerical results are provided to validate the effectiveness of the proposed ISAC waveform design for JCASAR systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.17627v1-abstract-full').style.display = 'none'; document.getElementById('2403.17627v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.14070">arXiv:2403.14070</a> <span> [<a href="https://arxiv.org/pdf/2403.14070">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> </div> </div> <p class="title is-5 mathjax"> QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Xiong%2C+Z">Zhuang Xiong</a>, <a href="/search/eess?searchtype=author&query=Jiang%2C+W">Wei Jiang</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+Y">Yang Gao</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Feng Liu</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+H">Hongfu Sun</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.14070v1-abstract-short" style="display: inline;"> Quantitative Susceptibility Mapping (QSM) dipole inversion is an ill-posed inverse problem for quantifying magnetic susceptibility distributions from MRI tissue phases. While supervised deep learning methods have shown success in specific QSM tasks, their generalizability across different acquisition scenarios remains constrained. Recent developments in diffusion models have demonstrated potential… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.14070v1-abstract-full').style.display = 'inline'; document.getElementById('2403.14070v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.14070v1-abstract-full" style="display: none;"> Quantitative Susceptibility Mapping (QSM) dipole inversion is an ill-posed inverse problem for quantifying magnetic susceptibility distributions from MRI tissue phases. While supervised deep learning methods have shown success in specific QSM tasks, their generalizability across different acquisition scenarios remains constrained. Recent developments in diffusion models have demonstrated potential for solving 2D medical imaging inverse problems. However, their application to 3D modalities, such as QSM, remains challenging due to high computational demands. In this work, we developed a 3D image patch-based diffusion model, namely QSMDiff, for robust QSM reconstruction across different scan parameters, alongside simultaneous super-resolution and image-denoising tasks. QSMDiff adopts unsupervised 3D image patch training and full-size measurement guidance during inference for controlled image generation. Evaluation on simulated and in-vivo human brains, using gradient-echo and echo-planar imaging sequences across different acquisition parameters, demonstrates superior performance. The method proposed in QSMDiff also holds promise for impacting other 3D medical imaging applications beyond QSM. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.14070v1-abstract-full').style.display = 'none'; document.getElementById('2403.14070v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.11187">arXiv:2403.11187</a> <span> [<a href="https://arxiv.org/pdf/2403.11187">pdf</a>, <a href="https://arxiv.org/format/2403.11187">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Task-Based Quantizer Design for Sensing With Random Signals </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ruan%2C+H">Hang Ruan</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.11187v1-abstract-short" style="display: inline;"> In integrated sensing and communication (ISAC) systems, random signaling is used to convey useful information as well as sense the environment. Such randomness poses challenges in various components in sensing signal processing. In this paper, we investigate quantizer design for sensing in ISAC systems. Unlike quantizers for channel estimation in massive multiple-input-multiple-out (MIMO) communic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.11187v1-abstract-full').style.display = 'inline'; document.getElementById('2403.11187v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.11187v1-abstract-full" style="display: none;"> In integrated sensing and communication (ISAC) systems, random signaling is used to convey useful information as well as sense the environment. Such randomness poses challenges in various components in sensing signal processing. In this paper, we investigate quantizer design for sensing in ISAC systems. Unlike quantizers for channel estimation in massive multiple-input-multiple-out (MIMO) communication systems, sensing in ISAC systems needs to deal with random nonorthogonal transmitted signals rather than a fixed orthogonal pilot. Considering sensing performance and hardware implementation, we focus on task-based hardware-limited quantization with spatial analog combining. We propose two strategies of quantizer optimization, i.e., data-dependent (DD) and data-independent (DI). The former achieves optimized sensing performance with high implementation overhead. To reduce hardware complexity, the latter optimizes the quantizer with respect to the random signal from a stochastic perspective. We derive the optimal quantizers for both strategies and formulate an algorithm based on sample average approximation (SAA) to solve the optimization in the DI strategy. Numerical results show that the optimized quantizers outperform digital-only quantizers in terms of sensing performance. Additionally, the DI strategy, despite its lower computational complexity compared to the DD strategy, achieves near-optimal sensing performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.11187v1-abstract-full').style.display = 'none'; document.getElementById('2403.11187v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.07125">arXiv:2403.07125</a> <span> [<a href="https://arxiv.org/pdf/2403.07125">pdf</a>, <a href="https://arxiv.org/format/2403.07125">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Learning-Aided Control of Robotic Tether-Net with Maneuverable Nodes to Capture Large Space Debris </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Boonrath%2C+A">Achira Boonrath</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Feng Liu</a>, <a href="/search/eess?searchtype=author&query=Botta%2C+E+M">Elenora M. Botta</a>, <a href="/search/eess?searchtype=author&query=Chowdhury%2C+S">Souma Chowdhury</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.07125v1-abstract-short" style="display: inline;"> Maneuverable tether-net systems launched from an unmanned spacecraft offer a promising solution for the active removal of large space debris. Guaranteeing the successful capture of such space debris is dependent on the ability to reliably maneuver the tether-net system -- a flexible, many-DoF (thus complex) system -- for a wide range of launch scenarios. Here, scenarios are defined by the relative… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07125v1-abstract-full').style.display = 'inline'; document.getElementById('2403.07125v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.07125v1-abstract-full" style="display: none;"> Maneuverable tether-net systems launched from an unmanned spacecraft offer a promising solution for the active removal of large space debris. Guaranteeing the successful capture of such space debris is dependent on the ability to reliably maneuver the tether-net system -- a flexible, many-DoF (thus complex) system -- for a wide range of launch scenarios. Here, scenarios are defined by the relative location of the debris with respect to the chaser spacecraft. This paper represents and solves this problem as a hierarchically decentralized implementation of robotic trajectory planning and control and demonstrates the effectiveness of the approach when applied to two different tether-net systems, with 4 and 8 maneuverable units (MUs), respectively. Reinforcement learning (policy gradient) is used to design the centralized trajectory planner that, based on the relative location of the target debris at the launch of the net, computes the final aiming positions of each MU, from which their trajectory can be derived. Each MU then seeks to follow its assigned trajectory by using a decentralized PID controller that outputs the MU's thrust vector and is informed by noisy sensor feedback (for realism) of its relative location. System performance is assessed in terms of capture success and overall fuel consumption by the MUs. Reward shaping and surrogate models are used to respectively guide and speed up the RL process. Simulation-based experiments show that this approach allows the successful capture of debris at fuel costs that are notably lower than nominal baselines, including in scenarios where the debris is significantly off-centered compared to the approaching chaser spacecraft. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07125v1-abstract-full').style.display = 'none'; document.getElementById('2403.07125v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper was accepted for presentation in proceedings of IEEE International Conference on Robotics and Automation 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.02565">arXiv:2403.02565</a> <span> [<a href="https://arxiv.org/pdf/2403.02565">pdf</a>, <a href="https://arxiv.org/format/2403.02565">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Deep Cooperation in ISAC System: Resource, Node and Infrastructure Perspectives </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wei%2C+Z">Zhiqing Wei</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+H">Haotian Liu</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+Z">Zhiyong Feng</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+H">Huici Wu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Q">Qixun Zhang</a>, <a href="/search/eess?searchtype=author&query=Du%2C+Y">Yucong Du</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.02565v3-abstract-short" style="display: inline;"> With the emerging Integrated Sensing and Communication (ISAC) technique, exploiting the mobile communication system with multi-domain resources, multiple network elements, and large-scale infrastructures to realize cooperative sensing is a crucial approach satisfying the requirements of high-accuracy and large-scale sensing in IoE. In this article, the deep cooperation in ISAC system including thr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.02565v3-abstract-full').style.display = 'inline'; document.getElementById('2403.02565v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.02565v3-abstract-full" style="display: none;"> With the emerging Integrated Sensing and Communication (ISAC) technique, exploiting the mobile communication system with multi-domain resources, multiple network elements, and large-scale infrastructures to realize cooperative sensing is a crucial approach satisfying the requirements of high-accuracy and large-scale sensing in IoE. In this article, the deep cooperation in ISAC system including three perspectives is investigated. In the microscopic perspective, namely, within a single node, the sensing information carried by time-frequency-space-code domain resources is processed, such as phase compensation, coherent accumulation and other operations, thereby improving the sensing accuracy. In the mesoscopic perspective, the sensing accuracy could be improved through the cooperation of multiple nodes. We explore various multi-node cooperative sensing scenarios and present the corresponding challenges and future research trends. In the macroscopic perspective, the massive number of infrastructures from the same operator or different operators could perform cooperative sensing to extend the sensing coverage and improve the sensing continuity. We investigate network architecture, target tracking methods, and the large-scale sensing assisted digital twin construction. Simulation results demonstrate the superiority of multi-nodes and multi-resources cooperative sensing over single resource or node sensing. This article may provide a deep and comprehensive view on the cooperative sensing in ISAC system to enhance the performance of sensing, supporting the applications of IoE. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.02565v3-abstract-full').style.display = 'none'; document.getElementById('2403.02565v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages and 6 figures, Accepted by IEEE Internet of Things Magazine</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.00339">arXiv:2403.00339</a> <span> [<a href="https://arxiv.org/pdf/2403.00339">pdf</a>, <a href="https://arxiv.org/format/2403.00339">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Energy-Efficient Clustered Cell-Free Networking with Access Point Selection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhou%2C+O">Ouyang Zhou</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+J">Junyuan Wang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fuqiang Liu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+J">Jiangzhou Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.00339v1-abstract-short" style="display: inline;"> Ultra-densely deploying access points (APs) to support the increasing data traffic would significantly escalate the cell-edge problem resulting from traditional cellular networks. By removing the cell boundaries and coordinating all APs for joint transmission, the cell-edge problem can be alleviated, which in turn leads to unaffordable system complexity and channel measurement overhead. A new scal… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.00339v1-abstract-full').style.display = 'inline'; document.getElementById('2403.00339v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.00339v1-abstract-full" style="display: none;"> Ultra-densely deploying access points (APs) to support the increasing data traffic would significantly escalate the cell-edge problem resulting from traditional cellular networks. By removing the cell boundaries and coordinating all APs for joint transmission, the cell-edge problem can be alleviated, which in turn leads to unaffordable system complexity and channel measurement overhead. A new scalable clustered cell-free network architecture has been proposed recently, under which the large-scale network is flexibly partitioned into a set of independent subnetworks operating parallelly. In this paper, we study the energy-efficient clustered cell-free networking problem with AP selection. Specifically, we propose a user-centric ratio-fixed AP-selection based clustering (UCR-ApSel) algorithm to form subnetworks dynamically. Following this, we analyze the average energy efficiency achieved with the proposed UCR-ApSel scheme theoretically and derive an effective closed-form upper-bound. Based on the analytical upper-bound expression, the optimal AP-selection ratio that maximizes the average energy efficiency is further derived as a simple explicit function of the total number of APs and the number of subnetworks. Simulation results demonstrate the effectiveness of the derived optimal AP-selection ratio and show that the proposed UCR-ApSel algorithm with the optimal AP-selection ratio achieves around 40% higher energy efficiency than the baselines. The analysis provides important insights to the design and optimization of future ultra-dense wireless communication systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.00339v1-abstract-full').style.display = 'none'; document.getElementById('2403.00339v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.00033">arXiv:2403.00033</a> <span> [<a href="https://arxiv.org/pdf/2403.00033">pdf</a>, <a href="https://arxiv.org/format/2403.00033">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Spatial Craving Patterns in Marijuana Users: Insights from fMRI Brain Connectivity Analysis with High-Order Graph Attention Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ding%2C+J">Jun-En Ding</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+S">Shihao Yang</a>, <a href="/search/eess?searchtype=author&query=Zilverstand%2C+A">Anna Zilverstand</a>, <a href="/search/eess?searchtype=author&query=Kulkarni%2C+K+R">Kaustubh R. Kulkarni</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+X">Xiaosi Gu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Feng Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.00033v5-abstract-short" style="display: inline;"> The excessive consumption of marijuana can induce substantial psychological and social consequences. In this investigation, we propose an elucidative framework termed high-order graph attention neural networks (HOGANN) for the classification of Marijuana addiction, coupled with an analysis of localized brain network communities exhibiting abnormal activities among chronic marijuana users. HOGANN i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.00033v5-abstract-full').style.display = 'inline'; document.getElementById('2403.00033v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.00033v5-abstract-full" style="display: none;"> The excessive consumption of marijuana can induce substantial psychological and social consequences. In this investigation, we propose an elucidative framework termed high-order graph attention neural networks (HOGANN) for the classification of Marijuana addiction, coupled with an analysis of localized brain network communities exhibiting abnormal activities among chronic marijuana users. HOGANN integrates dynamic intrinsic functional brain networks, estimated from functional magnetic resonance imaging (fMRI), using graph attention-based long short-term memory (GAT-LSTM) to capture temporal network dynamics. We employ a high-order attention module for information fusion and message passing among neighboring nodes, enhancing the network community analysis. Our model is validated across two distinct data cohorts, yielding substantially higher classification accuracy than benchmark algorithms. Furthermore, we discern the most pertinent subnetworks and cognitive regions affected by persistent marijuana consumption, indicating adverse effects on functional brain networks, particularly within the dorsal attention and frontoparietal networks. Intriguingly, our model demonstrates superior performance in cohorts exhibiting prolonged dependence, implying that prolonged marijuana usage induces more pronounced alterations in brain networks. The model proficiently identifies craving brain maps, thereby delineating critical brain regions for analysis <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.00033v5-abstract-full').style.display = 'none'; document.getElementById('2403.00033v5-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Liu%2C+F&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a 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